Causal Relationship Between Gut Microbiota and Acquired Immune Deficiency Syndrome: A Two-Sample Mendelian Randomization Study

Causal Relationship Between Gut Microbiota and Acquired Immune Deficiency Syndrome: A Two-Sample Mendelian Randomization Study | 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 Causal Relationship Between Gut Microbiota and Acquired Immune Deficiency Syndrome: A Two-Sample Mendelian Randomization Study Zhiwei Wang, Shuqi Meng, Yan Fan, Lina Zhao, Yan Cui, Ke-liang Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4493955/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Emerging evidence suggests that changes in the composition of the gut microbiota may not only be a consequence of AIDS but may also influence the risk of disease. However, it is not clear that these associations point to the certainty of causality. Objective To reveal the causal relationship between gut microbiota and AIDS, we performed a two-sample Mendelian randomization (MR) analysis. Materials And Methods We evaluated summary statistics of gut microbiota and HIV infection disease from published genome-wide association studies (GWAS). A two-sample MR analysis was performed to identify HIV-causing bacterial taxa in the samples based on inverse variance weighting (ivw) results. Sensitivity analyses were performed to verify the stability of the results. Finally, an inverse MR analysis was performed to assess the possibility of reverse causality. Results Combining the results of MR analysis and sensitivity analysis, we identified eight pathogenic bacterial genera: Subdoligaranulum (OR = 4.012,95% confidence interval [CI] = 1.783–9.027, P = 7.90E-04), Victivallis(OR = 1.605,95% CI = 1.012–2.547, P = 4.40E-02), and Ruminococcaceae_UCG-005 (OR = 2.051, 95% CI = 1.048–4.011, P = 3.60E-02) increased the risk of HIV infection. In contrast, genetically predicted Eggerthella (OR = 0.477, 95%CI = 0.283–0.805, P = 5.50E-03), Anaerotruncus (OR = 0.434, 95% CI = 0.197–0.954, P = 3.8E-02), Methanobrevibacter (OR = 0. 509 ; 95% CI = 0. 265 − 0.980; P = 4.30E-02), Clostridiumsensustricto1 (OR = 0.424, 95% CI = 0.182–0.988, P = 4.70E-02) and Coprococcus2 (OR = 0.377, 95% CI = 0.159–0.894, P = 2.70E-02) reduced the risk of HIV infection. Further sensitivity analyses verified the robustness of the above associations. Reverse MR analysis showed no evidence of reverse causality between HIV infection and the eight genera mentioned above. Conclusion This study demonstrates that Subdoligaranulum, Victivallis, Ruminococcaceae_UCG-005,Eggerthella, Clostridiumsensustricto1. Coprococcus2 and AIDS are causally linked, thus providing new insights into the mechanisms underlying the onset of gut microbiota-mediated HIV infection. Acquired Immune Deficiency Syndrome Gut microbiota Causal inference Mendelian randomization study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Since the first case of human immunodeficiency virus infection was reported in 1959, approximately 85 million people worldwide have been infected with the virus, and the number of people currently living with the infection is approximately 39 million. [ 1 ] As a virus primarily targeting CD4 + T cells, it is expected to cause progressive CD4 + T cell loss and widespread immune abnormalities in the host while increasing the risk of cardiac, skeletal, hepatic, renal, and neurological diseases. [ 2 ] Despite the proven effectiveness of antiretroviral therapy in suppressing HIV replication and restoring immune function, and the significant public health resources invested worldwide, the number of people in low- and middle-income developing countries who have access to treatment remains bleak. [ 1 ] The mucosa is one of the most important links in the HIV transmission pathway, and changes in the mucosal environment of the gastrointestinal tract after infection with the virus lead to dysbiosis and translocation of pre-existing microorganisms[ 3 , 4 ]. Although many high-quality cross-sectional studies have observed changes in the gut microbiota after HIV infection [ 5 – 9 ], recent studies have shown that the magnitude of changes in the composition of the gut microbiome is low during the acute phase of HIV infection [ 10 ] and that alterations in the microbiota specific to viral infection need to wait until the chronic HIV phase[ 11 ]. The results of several longitudinal cohort studies are available to provide evidence for this view[ 12 – 14 ], which may be influenced by a number of potential confounders, including age, geography, diet, and antibiotic use[ 15 , 16 ]. However, we believe that the line of research on the phenomenon of altered gut microbiota may need to shift from being caused by HIV infection to potentially contributing to HIV infection. While observational studies on changes in the gut microbiota of HIV-infected patients have been conducted for a long time[ 5 ], the specific causal associations between the two, which are influenced by a variety of confounding factors, are not clear. Mendelian randomization (MR), a method of data analysis that integrates genome-wide association studies (GWAS), utilizes the random assignment of single nucleotide polymorphisms (SNPs) at the time of conception and reduces the influence of external confounders so that genetic variation can be used as an instrumental variable to infer causality of exposure outcomes. From Mendel's second core hypothesis (i.e., the independence hypothesis: the instrumental variable is independent of the confounders affecting the exposure-outcome relationship), it follows that the inheritance of a trait is independent of the inheritance of other traits and random. Since the inheritance of one trait is independent of the inheritance of other traits and is random, it is unlikely that offspring genotypes will be associated with environmental confounders in the population[ 17 ]. This methodology will avoid the shortcomings of observational studies on the association of gut microbiome changes with HIV infection. Still, no Mendelian randomization studies are currently exploring the causal relationship between the two. For this reason, we conducted a two-sample Mendelian randomization (TSMR) study to clarify the possible role of microbiome changes in susceptibility to HIV infection using GWAS summary statistics from the MiBioGen and FinnGen consortia. Materials and Methods Study design and data sources To investigate the causal relationship between gut microbial genera and HIV disease risk using a two-sample MR approach, the study should have fulfilled the three assumptions of MR[ 18 ]: (1) the assumption of correlation: genetic variation used as an instrumental variable is associated with the gut microbiota; (2) the assumption of independence: the inclusion of instrumental variables is not subject to confounders; and (3) the assumption of exclusivity: there is no independent causal pathway between the genetic variation and AIDS, except through the gut microbiota. The flow chart of the study is shown in Fig. 1 . The MiBioGen consortium published the largest genome-wide meta-analysis of gut microbes targeting 16S rRNA gene sequencing to date in 2021[ 19 ], which encompassed 25 cohorts of 18,473 individuals, most of which were European populations. This GWAS pooled data contains a total of 211 microbial taxa at five levels of phylum (9), class (16), order (20), family (35), and genus (131) eligible for microbial quantitative trait locus (mbQTL) positional analysis. In this study, after excluding 12 unknown genera, we included single nucleotide polymorphisms (SNPs) of 119 genus-level gut microbiota as exposed genetic instrumental variables (IVs). The risk-associated SNPs for AIDS were obtained from a large HIV gwas from the Open gwas website, which included 357 cases and 218435 control European individuals[ 20 ]. Instrumental variable selection Based on the results of previous MR studies related to the gut microbiota, and to maximize the genetic variation explained by the genetic predictors, we chose SNPs with a significance threshold of less than 1.0 × 10 − 5 as instrumental variables for this study[ 21 – 25 ]. To eliminate the fact that genetic variants with similar genomic locations are more inclined to be co-inherited i.e. linkage disequilibrium (LD), the obtained SNPs were subjected to a clustering process, with the LD threshold for clustering set at r2 < 0.001 and the size of the clustered region range set at 10,000 kb. In addition, to ensure that the effects of SNPs on the exposures and the results correspond to the same alleles, the palindromic SNPs were excluded. In addition, we calculated the F-statistic to assess weak instrumental bias using the following equation[ 26 ]: F = Beta^2/SE^2 Where the beta value reflects the mean difference between the case and control groups, and the se value is the standard error between the two groups. An F statistic ≥ 10 indicates that there is no strong evidence of weak instrumental bias. IVs with an F statistic less than < 10 are considered weak IVs and were excluded. Statistical Analysis The causal effect relationship of gut microbiota on AIDS was assessed using the TwoSampleMR package in R software(version 4.0.2). The main assessment methods included MR-Egger regression, inverse variance weighting (IVW), weighted median, weighted model, and simple model. Among them we focused on the sensitivity analysis results of inverse variance weighted (IVW) meta-analyses and MR Egger regressions, where IVW corresponds to weighted regressions of the effect of exposure on the outcome with an intercept restriction of zero, while the estimates of the MR-Egger method are relatively robust to the presence of pleiotropy[ 27 ]. In the absence of horizontal pleiotropy, IVW-MR estimation is the most preferred choice for sensitivity analyses. Therefore for sensitivity analyses of SNPs, heterogeneity between SNPs included in the analysis was assessed using intercepts from IVW and MR Egger methods and quantified using Cochrane's Q-test. In addition, the MR-PRESSO method was used to assess and correct for overall horizontal pleiotropy, P > 0.05 indicates that there was no heterogeneity in the included IVs and the study's results do not need to take into account the effects caused by heterogeneity. Finally, leave-one-out sensitivity analyses were conducted to prevent potentially strong-influence SNPs from affecting the reliability and stability of causal effect estimates. Reverse Mendelian Randomization Analysis We performed additional reverse MR analyses to explore reverse causality. Significant reverse MR analyses indicated reverse causality from AIDS (as exposure) to microbiota characteristics (as outcome). The reverse MR analysis procedure was the same as the MR analysis procedure described above. Result Instrumental Variables Selection After the instrumental variable selection process on the exposure data, 42 genus-associated SNPs were selected (genome-wide statistical significance threshold, P < 1×10 − 5), and 12 SNPs were finally obtained after removing the linkage disequilibrium (r 2 < 0.001, kb = 10000). Meanwhile, for further analyses, we also collected other important information on the target SNPs including effector allele, other alleles, β, SE, and P value. Finally, the F-statistics of the selected SNPs all exceeded 10, indicating that there was no weak instrumental variable bias in our study(Table 1 ). The impact of the gut microbiota on AIDS Based on the IVW method, 8 genera of bacterial taxa were found to predict a correlation between gut microbiota and the risk of AIDS (Fig. 2 ). MR analysis shows that Subdoligaranulum increases the risk of AIDS (OR = 4 012; 95% confidence interval [CI] = 1.783–9.027; P = 7.90E -04), the weighted median method also obtained similar results (OR = 4.235; 95% confidence interval [CI] = 1.366–13.132; P = 1.20E-02). Victivallis increased the risk of AIDS (OR = 1.605; 95% CI = 1.012–2.547; P = 4.40E-02), which was further confirmed by the weighted median method (OR = 1.787; 95% CI = 1.002–3.186; P = 4.90E-02). Ruminococcaceae_UCG-005 also increased the risk of HIV infection (OR = 2.051; 95% CI = 1.048–4.011; P = 3.60E-02). In contrast, genetically predicted Eggerthella (OR = 0.477; 95% CI = 0.283–0.805; P = 5.50E-03), Anaerotruncus (OR = 0.434; 95% CI = 0.197–0.954), Methanobrevibacter (OR = 0. 509 ; 95% CI = 0.265–0.980; P = 4.30E-02), Clostridiumsensustricto1 (OR = 0.424; 95% CI = 0.182–0.988; P = 4.70E-02), and Coprococcus2 (OR = 0.377; 95% CI = 0.159–0.894; P = 2.70E-02) reduced the risk of AIDS (Table 1 ). Detailed statistics of the gut microbiota at all taxonomic levels are shown in Supplementary Table S1 . Sensitivity analyses Cochran Q statistic for Subdoligranulum ( P = 0.544), Eggerthella ( P = 0.800), Coprococcus2 ( P = 0.610), Ruminococcaceae_UCG-005 ( P = 0.568). Anaerotruncus ( P = 0.723), Methanobrevibacter ( P = 0.298), Victivallis ( P = 0.302), and Clostridiumsensustricto1 ( P = 0.483) Cochran's Q illustrates that the included IVs were not heterogeneous and had high reliability, and the results of the study did not need to take into account the effects caused by heterogeneity (Table 1 ). MR-PRESSO had no significant effect on the quality of Subdoligranulum ( P = 0.541), Eggerthella ( P = 0.808), Coprococcus2 ( P = 0.632), Ruminococcaceae_UCG-005 ( P = 0.592), Anaerotruncus ( P = 0.729), Metacercaria ( P = 0.729), and Metacercaria ( P = 0.483), Methanobrevibacter ( P = 0.36), Victivallis ( P = 0.32), and Clostridiumsensustricto1 ( P = 0.509) test analyses did not reveal significant horizontal pleiotropy between the instrumental variables and the results (Table 1 ). In addition to this, we conducted a leave-one-out sensitivity analysis using the IVW method to observe whether the results changed after removing each SNP. The results showed that the results did not change much after progressively removing a particular SNP. No SNP had a disproportionate effect on the total estimate (Figure. 3). Scatter plots showed the magnitude of the effect of gut microbiota SNPs on the estimated HIV risk (Fig. 4 ). Based on the results of the reverse MR analyses, there was no evidence of a significant causal effect of AIDS on gut-to-gut microbiota. Also, there was no significant heterogeneity and horizontal pleiotropy in the sensitivity analyses regarding the AIDS instrumental variables (Table 3 ). Detailed information about the IVs used in the reverse MR analysis is shown in Supplementary Table S2 . Discussion As far as we know, this two-sample MR study is the first to examine the causal association between gut microbiota and people living with HIV by using a publicly available genetic database of European populations. Our findings suggest that Subdoligranulum, Eggerthella, Coprococcus2, Ruminococcaceae_UCG-005, Anaerotruncus, Methanobrevibacter, Victivallis, and Clostridiumsensustricto1 microbial genera are causally associated with AIDS, validating the findings of many previous studies with the new identification of gut microbial genera associated with the risk of developing AIDS. This enhances the understanding of the role of gut microbiota in the pathogenesis of AIDS and provides new insights into the prevention and diagnosis of AIDS. As an important route of HIV transmission and a major site of early replication, alterations in the mucosal microbiota environment of the gastrointestinal tract may be closely related to the development of AIDS[ 3 , 28 ]. Studies on the gut microbiota and the risk of AIDS have a long history, and an increase in potentially pathogenic Aspergillus and Prevotella spp. and a decrease in commensal organisms such as Mycobacterium avium and Mycobacterium thicketi have been reported in people living with HIV [ 7 , 8 , 29 , 30 ]. However, the strength of evidence from such cross-sectional studies may be limited by diet, age, diet, geographic location, and multiple other confounding factors[ 15 , 16 ]. Genetic variation in humans is present at birth and remains stable throughout the life cycle. In the Mendelian randomization framework, genotypes (determined by genetic variation) are randomly assigned to offspring during meiosis, making them largely independent of confounding factors that typically plague observational epidemiological studies in large-scale populations[ 31 ]. In addition, germline genetic variations (i.e. heritable variations) are temporarily fixed during conception and will not change due to any outcome or disease, thus ruling out the possibility of reverse causality. In addition, due to the improvement of modern genotyping technology, the measurement error and systematic misclassification of genetic data are usually low. Furthermore, Mendelian randomization can be considered similar to "natural randomized controlled trials". Therefore, associations obtained by MR methods are less affected by confounding factors. Notably, it has been shown that populations have limited changes in gut microbiota components during the acute phase of HIV infection and that specific gut microbial changes associated with HIV infection may not be apparent until the chronic phase, demonstrating that the observed differences in gut microbiota may pre-exist[ 10 ]. This gives reason to believe that changes in the gut microbiota may influence the course of the AIDS development. Thus, in the present study, we used human genus-level gut flora sequencing data as an exposure factor to assess its impact on the outcome of AIDS. As one of the gut microorganisms with the ability to produce butyrate[ 32 ], Subdoligleagum is strongly associated with circulating CD4 + T cell counts and T cell immune activation. Enrichment of this bacterium may lead to persistent CD4 + T-cell depletion after HIV infection and immune unresponsiveness due to poor CD4 + T-cell reconstitution even after effective antiretroviral therapy[ 33 ]. Therefore, Subdoligleagum may be a risk factor for HIV infection, but the underlying mechanisms remain to be elucidated by further studies. The advent of antiretroviral therapy, currently the primary option for HIV treatment, has significantly improved the incidence of poor prognosis in people living with HIV[ 34 – 36 ]. However, with the onset of physiologic aging, People living with HIV (PLWH) experience persistent low-grade inflammation and increased risk of other comorbidities[ 37 – 39 ]. This suggests that the gut microbiota may be affected by a combination of infection and age[ 40 ]. In a study comparing bacterial profiles in the feces of elderly PLWH with those of HIV-uninfected older adults (controls), the abundance of Eggertella spp. was lower in PLWH versus controls. Villanueva et al. also found that compared with uninfected controls, patients with PLWH treated with protease inhibitors in combination with nucleoside/tide reverse transcriptase inhibitors (NRTI) had a higher abundance of Eggertella was higher, but there was no difference in patients treated with other NRTI combinations[ 41 ]. Whereas our study demonstrated that Eggertella may be a protective factor in reducing the risk of AIDS, early intervention against this genus may be a target for improving persistent inflammation and comorbidities in late-stage PLWH. One study concluded that a reduction in Coprococcus 2, a member of the obesogenic microbiota, increased the risk of metabolic syndrome twofold in HIV-infected individuals[ 42 ]. Petersen et al.’s study [ 43 ]demonstrated that the growth of Vibrio vulnificus and the reduction of several species of Clostridium were sufficient to trigger fat accumulation and metabolic syndrome in immunodeficient mice, suggesting that Coprococcus 2 may be able to play a protective role by reducing metabolic risk in immunodeficient patients. Moreover, Cohort studies on the early post-HIV infection period have shown a reduced abundance of the Ruminococceae family[ 42 ]. This seems to be inconsistent with the finding of Ruminococcaceae_UCG-005 as an exposure risk factor for AIDS in this study. It is known from the current study that Ruminococcus spp. produce short-chain fatty acids (SCFA) through carbohydrate fermentation[ 44 ], but the role of SCFA in inflammation has not been clarified, and it may play multiple roles in different environmental conditions and cell types. The prevailing view is that SCFA is anti-inflammatory, but when the intestinal epithelium is infected or damaged by bacteria, SCFA may be pro-inflammatory, which may partly explain the discrepancy in the results of different studies on the role of these bacteria in inflammation[ 45 , 46 ]. Charlene et al. [ 47 ]in their assessment of the interrelationships between HIV and iron status and gut microbiota composition in non-school-aged children in South Africa found a lower relative abundance of the butyrate-producing genera Anaerostipes and Anaerotruncus in iron deficiency as compared to iron-sufficient children, which is in agreement with our findings. Methanobrevibacter is a dominant archaeon commonly found in healthy populations, and its main species Mycobacterium smegmatis, has been well studied, its main role is to remove dihydrogen from the host intestinal environment and to promote the production of acetate, butyrate, and ATP[ 48 ]. It has been associated with obesity, colorectal cancer, anorexia, inflammatory bowel disease, irritable bowel syndrome, etc[ 49 , 50 ]. Simin et al.[ 51 ] suggest that this bacterium may contribute to the cognitive deficits that occur in PLWH and that it exerts a protective effect through immune activation causing chronic inflammation. An observational study reporting a significant enrichment of microbiota composition in HIV-exposed seronegative individuals (HESN) with Victivalis (p = 0.0029) compared to healthy controls and those with progressed HIV infection suggests a role in modulating the risk of viral infection and controlling the extent of infection[ 52 ]. This is in contradiction with our findings, but considering that the genus Victoralis is also a known producer of SCFA[ 53 ] and that the explanation for its protective effects stems from the anti-inflammatory, anti-tumor, and antimicrobial effects of SCFA, as well as its ability to maintain intestinal integrity and immune homeostasis[ 54 ]. Considering the variable role of SCFA and the specificity of the HESN population, it is reasonable to further explore the impact Victivallis plays in HIV infection. Another SCFA-producing genus[ 55 ], Clostridium sensustricto1 was identified by us as a protective factor for reducing AIDS risk. There are a couple of redeeming features of this study. First, the interference of confounding factors on causality was minimized by the stability of SNPs. Second, the research data used were the largest, most comprehensive, and widely selected to ensure the reliability of the results. Thirdly, this study used the classical two-sample MR analysis method, which was paired with a variety of sensitivity checks such as MR-PRESSO and MR-Egger regression intercept terms. Finally, inverse causality analysis inference was also performed to provide accurate quantitative estimates of causality. Our findings suggest that enhanced surveillance of the gut microbiota in specific populations may help reduce the risk of AIDS incidence. However, our study has several limitations. First, the majority of individuals in the GWAS pooled data used in this study were European populations, and the conclusions drawn from this need to be validated on other races. Second, We only analyzed bacterial taxa at the genus level, which is mainly a reference to the selection of other studies[ 21 – 25 ], and it is necessary to further explore the causal relationship between the gut microbiota and the risk of HIV disease at the level of a wider range of species taxa to obtain more complete conclusions. To obtain more SNPs as instrumental variables to fulfill the requirements of sensitivity analysis and horizontal pleiotropy testing, we did not use the traditional GWAS significance threshold (P < 5 × 10 − 8) to screen for SNPs. For this reason, there may be a lack of statistical efficacy in the analysis results, but we subsequently utilized multiple correction methods to limit the occurrence of false negatives as much as possible. Third, considering the contradictions in the conclusions we drew in the SCFA-producing genus species (Ruminococcaceae_UCG-005, Victivallis, and Clostridiumsensustricto1 ), taking into account the fact that SCFA may play different roles in different scenarios, and the role of SCFA in signaling due to the different ligand-receptor pair combinations during signaling may result in the induction of different signaling cascades in different immune cells[ 56 ]. Therefore, more detailed studies in this area are needed to clarify the mechanism of action and species-specific effects. Conclusion In conclusion, this study demonstrated the causal relationship between gut microbiota and AIDS through MR analysis, revealed the influence of specific gut microbiota taxa on the risk of AIDS, and, based on a large number of previous observational studies on gut microbiota changes after HIV infection, put forward the viewpoint that alterations in gut microbiota affect the risk of AIDS, which provides new evidence and research ideas for the prevention of the disease. Declarations Ethical Approval The research data involving human participants in this study are published data for which informed consent has been obtained from the participants. Written informed consent was not required for participation in the studies, in accordance with national legislative and institutional requirements. Funding This work was supported by the grant from the National Natural Science Foundation of China (81772043, 81971879). Acknowledgements The data analyzed in this study were provided by the MiBioGen consortium with the Open Gwas website. We thank them for their contribution to the study and the participants of the corresponding study, without whom this work would not have been possible. Declaration of interests The authors have no conflicts of interest to declare. References "UN Joint Programme on HIV/AIDS. "[Online] . Available from: https://www.unaids.org/en/resources/fact-sheet . Deeks, S.G., et al., HIV infection . Nat Rev Dis Primers, 2015. 1: p. 15035. Veazey, R.S., et al., Gastrointestinal tract as a major site of CD4 + T cell depletion and viral replication in SIV infection . Science, 1998. 280(5362): p. 427–31. Chege, D., et al., Sigmoid Th17 populations, the HIV latent reservoir, and microbial translocation in men on long-term antiretroviral therapy . Aids, 2011. 25(6): p. 741–9. Brenchley, J.M., et al., Microbial translocation is a cause of systemic immune activation in chronic HIV infection . Nat Med, 2006. 12(12): p. 1365–71. Kim, C.J., et al., Mucosal Th17 cell function is altered during HIV infection and is an independent predictor of systemic immune activation . J Immunol, 2013. 191(5): p. 2164–73. Dillon, S.M., et al., An altered intestinal mucosal microbiome in HIV-1 infection is associated with mucosal and systemic immune activation and endotoxemia . Mucosal Immunol, 2014. 7(4): p. 983–94. Lozupone, C.A., et al., Alterations in the gut microbiota associated with HIV-1 infection . Cell Host Microbe, 2013. 14(3): p. 329–39. McHardy, I.H., et al., HIV Infection is associated with compositional and functional shifts in the rectal mucosal microbiota . Microbiome, 2013. 1(1): p. 26. Fulcher, J.A., et al., Gut dysbiosis and inflammatory blood markers precede HIV with limited changes after early seroconversion . EBioMedicine, 2022. 84: p. 104286. Rocafort, M., et al., Evolution of the gut microbiome following acute HIV-1 infection . Microbiome, 2019. 7(1): p. 73. Chen, Y., et al., Signature changes in gut microbiome are associated with increased susceptibility to HIV-1 infection in MSM . Microbiome, 2021. 9(1): p. 237. Handley, S.A., et al., SIV Infection-Mediated Changes in Gastrointestinal Bacterial Microbiome and Virome Are Associated with Immunodeficiency and Prevented by Vaccination . Cell Host Microbe, 2016. 19(3): p. 323–35. Klase, Z., et al., Dysbiotic bacteria translocate in progressive SIV infection . Mucosal Immunol, 2015. 8(5): p. 1009–20. Yatsunenko, T., et al., Human gut microbiome viewed across age and geography . Nature, 2012. 486(7402): p. 222–7. Vujkovic-Cvijin, I., et al., Host variables confound gut microbiota studies of human disease . Nature, 2020. 587(7834): p. 448–454. Burgess S, T.S., Mendelian randomization: methods for causal inference using genetic variants: CRC Press ;. 2021. Davies, N.M., M.V. Holmes, and G. Davey Smith, Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians . Bmj, 2018. 362: p. k601. Kurilshikov, A., et al., Large-scale association analyses identify host factors influencing human gut microbiome composition . Nat Genet, 2021. 53(2): p. 156–165. “Human immunodeficiency virus [HIV] disease gwas datasets"[online] . Available from: https://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_HIV/ . Ni, J.J., et al., Gut Microbiota and Psychiatric Disorders: A Two-Sample Mendelian Randomization Study . Front Microbiol, 2021. 12: p. 737197. Song, J., et al., The causal links between gut microbiota and COVID-19: A Mendelian randomization study . J Med Virol, 2023. 95(5): p. e28784. Yang, M., et al., No Evidence of a Genetic Causal Relationship between Ankylosing Spondylitis and Gut Microbiota: A Two-Sample Mendelian Randomization Study . Nutrients, 2023. 15(4). Luo, M., et al., Causal effects of gut microbiota on the risk of chronic kidney disease: a Mendelian randomization study . Front Cell Infect Microbiol, 2023. 13: p. 1142140. Gagnon, E., et al., Impact of the gut microbiota and associated metabolites on cardiometabolic traits, chronic diseases and human longevity: a Mendelian randomization study . J Transl Med, 2023. 21(1): p. 60. Burgess, S. and S.G. Thompson, Avoiding bias from weak instruments in Mendelian randomization studies . Int J Epidemiol, 2011. 40(3): p. 755–64. Bowden, J., G. Davey Smith, and S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression . Int J Epidemiol, 2015. 44(2): p. 512–25. Prendergast, A., et al., HIV-1 infection is characterized by profound depletion of CD161 + Th17 cells and gradual decline in regulatory T cells . Aids, 2010. 24(4): p. 491–502. Vujkovic-Cvijin, I., et al., Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism . Sci Transl Med, 2013. 5(193): p. 193ra91. Mutlu, E.A., et al., A compositional look at the human gastrointestinal microbiome and immune activation parameters in HIV infected subjects . PLoS Pathog, 2014. 10(2): p. e1003829. Sekula, P., et al., Mendelian Randomization as an Approach to Assess Causality Using Observational Data . J Am Soc Nephrol, 2016. 27(11): p. 3253–3265. Eeckhaut, V., et al., Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum . Microb Biotechnol, 2011. 4(4): p. 503–12. Lu, W., et al., Association Between Gut Microbiota and CD4 Recovery in HIV-1 Infected Patients . Front Microbiol, 2018. 9: p. 1451. Lundgren, J.D., et al., Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection . N Engl J Med, 2015. 373(9): p. 795–807. Simon, V., D.D. Ho, and Q. Abdool Karim, HIV/AIDS epidemiology, pathogenesis, prevention, and treatment . Lancet, 2006. 368(9534): p. 489–504. Walker, B.D. and M.S. Hirsch, Antiretroviral therapy in early HIV infection . N Engl J Med, 2013. 368(3): p. 279–81. Deeks, S.G., R. Tracy, and D.C. Douek, Systemic effects of inflammation on health during chronic HIV infection . Immunity, 2013. 39(4): p. 633–45. Buford, T.W., (Dis)Trust your gut: the gut microbiome in age-related inflammation, health, and disease . Microbiome, 2017. 5(1): p. 80. Zevin, A.S., et al., Microbial translocation and microbiome dysbiosis in HIV-associated immune activation . Curr Opin HIV AIDS, 2016. 11(2): p. 182–90. Liu, J., et al., Among older adults, age-related changes in the stool microbiome differ by HIV-1 serostatus . EBioMedicine, 2019. 40: p. 583–594. Villanueva-Millán, M.J., et al., Differential effects of antiretrovirals on microbial translocation and gut microbiota composition of HIV-infected patients . J Int AIDS Soc, 2017. 20(1): p. 21526. Gelpi, M., et al., Impact of Human Immunodeficiency Virus-Related Gut Microbiota Alterations on Metabolic Comorbid Conditions . Clin Infect Dis, 2020. 71(8): p. e359-e367. Petersen, C., et al., T cell-mediated regulation of the microbiota protects against obesity . Science, 2019. 365(6451). Ze, X., et al., Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon . Isme j, 2012. 6(8): p. 1535–43. Vinolo, M.A., et al., Regulation of inflammation by short chain fatty acids . Nutrients, 2011. 3(10): p. 858–76. Morrison, D.J. and T. Preston, Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism . Gut Microbes, 2016. 7(3): p. 189–200. Goosen, C., et al., Associations of HIV and iron status with gut microbiota composition, gut inflammation and gut integrity in South African school-age children: a two-way factorial case-control study . J Hum Nutr Diet, 2023. 36(3): p. 819–832. Gaci, N., et al., Archaea and the human gut: new beginning of an old story . World J Gastroenterol, 2014. 20(43): p. 16062–78. Dridi, B., et al., High prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the human gut using an improved DNA detection protocol . PLoS One, 2009. 4(9): p. e7063. Camara, A., et al., Clinical evidence of the role of Methanobrevibacter smithii in severe acute malnutrition . Sci Rep, 2021. 11(1): p. 5426. Hua, S., et al., Gut Microbiota and Cognitive Function Among Women Living with HIV . J Alzheimers Dis, 2023. 95(3): p. 1147–1161. Lopera, T.J., et al., A specific structure and high richness characterize intestinal microbiota of HIV-exposed seronegative individuals . PLoS One, 2021. 16(12): p. e0260729. Zoetendal, E.G., et al., Victivallis vadensis gen. nov., sp. nov., a sugar-fermenting anaerobe from human faeces . Int J Syst Evol Microbiol, 2003. 53(Pt 1): p. 211–215. Yuille, S., et al., Human gut bacteria as potent class I histone deacetylase inhibitors in vitro through production of butyric acid and valeric acid . PLoS One, 2018. 13(7): p. e0201073. Rivera-Chávez, F., et al., Depletion of Butyrate-Producing Clostridia from the Gut Microbiota Drives an Aerobic Luminal Expansion of Salmonella . Cell Host Microbe, 2016. 19(4): p. 443–54. Enriquez, A.B., et al., Regulation of Immune Homeostasis, Inflammation, and HIV Persistence by the Microbiome, Short-Chain Fatty Acids, and Bile Acids . Annu Rev Virol, 2023. 10(1): p. 397–422. Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table 1 Mendelian randomisation results for the causal effect between gut microbiota and the risk of AIDS. Table2.xlsx Table 2 SNPs used as instrumental variables from gut microbiome and AIDS GWASs Table3.xlsx Table 3 Analysis of reverse causality between AIDS and gut microbiota. SupplementaryTableS1.csv supplementaryTableS2.csv Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-4493955","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312237749,"identity":"c9554daa-1605-4c03-aebf-be29200e8e0d","order_by":0,"name":"Zhiwei Wang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Wang","suffix":""},{"id":312237752,"identity":"03004701-ab8a-4e06-b0ae-d185cbae0d3f","order_by":1,"name":"Shuqi Meng","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuqi","middleName":"","lastName":"Meng","suffix":""},{"id":312237753,"identity":"0e457f1b-e8a3-4775-913e-cc215e77cb88","order_by":2,"name":"Yan Fan","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Fan","suffix":""},{"id":312237754,"identity":"73c1ccba-b106-4308-92be-bcbf5405b7cf","order_by":3,"name":"Lina Zhao","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Zhao","suffix":""},{"id":312237755,"identity":"eaa30fb6-ed1f-417e-8111-b329ca4075e6","order_by":4,"name":"Yan Cui","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Cui","suffix":""},{"id":312237756,"identity":"4e4f9f72-7505-44fb-9210-ffeca5d3fcf6","order_by":5,"name":"Ke-liang Xie","email":"data:image/png;base64,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","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ke-liang","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-05-29 03:18:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4493955/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4493955/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58265968,"identity":"4cd8d8d1-56d4-42ca-bf0d-9e836baa82a4","added_by":"auto","created_at":"2024-06-13 07:23:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46939,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the study. The whole workflow of MR analysis. MR, Mendelian randomization; AIDS, Acquired immune deficiency syndrome.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/ab2bb9c6b196c025ec9bd04b.png"},{"id":58266683,"identity":"027a03e7-5b26-4ba2-900a-dd0d7f1a7ffc","added_by":"auto","created_at":"2024-06-13 07:31:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":566020,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for the causal effect between gut microbiota and HIV risk.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/9889968361909b2391e54210.png"},{"id":58265973,"identity":"dce94b68-d78b-47f2-afeb-a7e0ce50184d","added_by":"auto","created_at":"2024-06-13 07:23:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7641189,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment and visualization of gut microbiota susceptibility to HIV based on the leave-one-out method. (A-H) Subdoligranulum, Eggerthella, Coprococcus2, Ruminococcaceae_UCG-005, Anaerotruncus, Methanobrevibacter, Victivallis, Clostridium sensustricto1 Causal estimates calculated based on the IVW model. The causal estimates calculated by sensustricto1 based on the IVW model, as well as the overall estimates (red horizontal line), were not disproportionately driven by the removal of individual variants (black horizontal line), suggesting that there was no significant heterogeneity in the SNPs included in the study.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/cc174bbe3fc103de2eacc9ab.png"},{"id":58265971,"identity":"ecb4b723-de8c-4ff3-9194-a4a837e11307","added_by":"auto","created_at":"2024-06-13 07:23:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4149496,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of MR estimates of significant causal associations between eight gut microbiota taxa and HIV risk. (A-H) Subdoligranulum, Eggerthella, Coprococcus2, Ruminococcaceae_UCG-005, Anaerotruncus, Methanobrevibacter, Victivallis, Clostridium sensustricto1 Causal effect of the genus on AIDS; the direction moving diagonally upwards from left to right is a positive correlation line, suggesting that the gut microbiota contributes to AIDS. Negative correlations moving diagonally downward from left to right indicate that the gut microbiota has an inhibitory effect on HIV. The 95% confidence intervals for each correlation are shown as horizontal and vertical lines.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/2b4014a592b882e55afe47ce.png"},{"id":59045627,"identity":"3a58b833-5a8b-4793-b6ae-f0f7cd920695","added_by":"auto","created_at":"2024-06-25 18:19:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13922909,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/6867a3cc-d734-4d1e-b181-cc0074639a2a.pdf"},{"id":58266686,"identity":"05021eb3-611a-4a2a-86ed-eed27b8a1b02","added_by":"auto","created_at":"2024-06-13 07:31:01","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1 \u003c/strong\u003eMendelian randomisation results for the causal effect between gut microbiota and the risk of AIDS.\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/2d82c6469d72f3fe2dbbbc4c.xlsx"},{"id":58266684,"identity":"1c0bc813-14cd-4507-8f20-05fdbf5c13f1","added_by":"auto","created_at":"2024-06-13 07:31:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2 \u003c/strong\u003eSNPs used as instrumental variables from gut microbiome and AIDS GWASs\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/f7466088506f2eade7c59477.xlsx"},{"id":58267244,"identity":"2dfd314d-c2c3-4406-a046-468aba2cbf3d","added_by":"auto","created_at":"2024-06-13 07:39:01","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Analysis of reverse causality between AIDS and gut microbiota.\u003c/p\u003e","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/39da4caec1b9b6fc70ffc115.xlsx"},{"id":58265974,"identity":"e7d5cc9f-8ae6-4fae-8852-2bce04504eee","added_by":"auto","created_at":"2024-06-13 07:23:01","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":122473,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/ccd99e2b82e2a5a114deb523.csv"},{"id":58265975,"identity":"594e8acb-9ad6-4562-80e4-c0243ee05b66","added_by":"auto","created_at":"2024-06-13 07:23:01","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10365,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryTableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4493955/v1/74854e5ad8634b9d0c9df7c7.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Relationship Between Gut Microbiota and Acquired Immune Deficiency Syndrome: A Two-Sample Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the first case of human immunodeficiency virus infection was reported in 1959, approximately 85\u0026nbsp;million people worldwide have been infected with the virus, and the number of people currently living with the infection is approximately 39\u0026nbsp;million. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] As a virus primarily targeting CD4\u0026thinsp;+\u0026thinsp;T cells, it is expected to cause progressive CD4\u0026thinsp;+\u0026thinsp;T cell loss and widespread immune abnormalities in the host while increasing the risk of cardiac, skeletal, hepatic, renal, and neurological diseases. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Despite the proven effectiveness of antiretroviral therapy in suppressing HIV replication and restoring immune function, and the significant public health resources invested worldwide, the number of people in low- and middle-income developing countries who have access to treatment remains bleak. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The mucosa is one of the most important links in the HIV transmission pathway, and changes in the mucosal environment of the gastrointestinal tract after infection with the virus lead to dysbiosis and translocation of pre-existing microorganisms[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although many high-quality cross-sectional studies have observed changes in the gut microbiota after HIV infection [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], recent studies have shown that the magnitude of changes in the composition of the gut microbiome is low during the acute phase of HIV infection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and that alterations in the microbiota specific to viral infection need to wait until the chronic HIV phase[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The results of several longitudinal cohort studies are available to provide evidence for this view[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which may be influenced by a number of potential confounders, including age, geography, diet, and antibiotic use[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, we believe that the line of research on the phenomenon of altered gut microbiota may need to shift from being caused by HIV infection to potentially contributing to HIV infection.\u003c/p\u003e \u003cp\u003eWhile observational studies on changes in the gut microbiota of HIV-infected patients have been conducted for a long time[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the specific causal associations between the two, which are influenced by a variety of confounding factors, are not clear. Mendelian randomization (MR), a method of data analysis that integrates genome-wide association studies (GWAS), utilizes the random assignment of single nucleotide polymorphisms (SNPs) at the time of conception and reduces the influence of external confounders so that genetic variation can be used as an instrumental variable to infer causality of exposure outcomes. From Mendel's second core hypothesis (i.e., the independence hypothesis: the instrumental variable is independent of the confounders affecting the exposure-outcome relationship), it follows that the inheritance of a trait is independent of the inheritance of other traits and random. Since the inheritance of one trait is independent of the inheritance of other traits and is random, it is unlikely that offspring genotypes will be associated with environmental confounders in the population[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This methodology will avoid the shortcomings of observational studies on the association of gut microbiome changes with HIV infection. Still, no Mendelian randomization studies are currently exploring the causal relationship between the two. For this reason, we conducted a two-sample Mendelian randomization (TSMR) study to clarify the possible role of microbiome changes in susceptibility to HIV infection using GWAS summary statistics from the MiBioGen and FinnGen consortia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data sources\u003c/h2\u003e \u003cp\u003eTo investigate the causal relationship between gut microbial genera and HIV disease risk using a two-sample MR approach, the study should have fulfilled the three assumptions of MR[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]: (1) the assumption of correlation: genetic variation used as an instrumental variable is associated with the gut microbiota; (2) the assumption of independence: the inclusion of instrumental variables is not subject to confounders; and (3) the assumption of exclusivity: there is no independent causal pathway between the genetic variation and AIDS, except through the gut microbiota. The flow chart of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe MiBioGen consortium published the largest genome-wide meta-analysis of gut microbes targeting 16S rRNA gene sequencing to date in 2021[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which encompassed 25 cohorts of 18,473 individuals, most of which were European populations. This GWAS pooled data contains a total of 211 microbial taxa at five levels of phylum (9), class (16), order (20), family (35), and genus (131) eligible for microbial quantitative trait locus (mbQTL) positional analysis. In this study, after excluding 12 unknown genera, we included single nucleotide polymorphisms (SNPs) of 119 genus-level gut microbiota as exposed genetic instrumental variables (IVs). The risk-associated SNPs for AIDS were obtained from a large HIV gwas from the Open gwas website, which included 357 cases and 218435 control European individuals[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable selection\u003c/h2\u003e \u003cp\u003eBased on the results of previous MR studies related to the gut microbiota, and to maximize the genetic variation explained by the genetic predictors, we chose SNPs with a significance threshold of less than 1.0 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5 as instrumental variables for this study[\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo eliminate the fact that genetic variants with similar genomic locations are more inclined to be co-inherited i.e. linkage disequilibrium (LD), the obtained SNPs were subjected to a clustering process, with the LD threshold for clustering set at r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and the size of the clustered region range set at 10,000 kb. In addition, to ensure that the effects of SNPs on the exposures and the results correspond to the same alleles, the palindromic SNPs were excluded. In addition, we calculated the F-statistic to assess weak instrumental bias using the following equation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;Beta^2/SE^2\u003c/p\u003e \u003cp\u003eWhere the beta value reflects the mean difference between the case and control groups, and the se value is the standard error between the two groups. An F statistic\u0026thinsp;\u0026ge;\u0026thinsp;10 indicates that there is no strong evidence of weak instrumental bias. IVs with an F statistic less than \u0026lt;\u0026thinsp;10 are considered weak IVs and were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe causal effect relationship of gut microbiota on AIDS was assessed using the TwoSampleMR package in R software(version 4.0.2). The main assessment methods included MR-Egger regression, inverse variance weighting (IVW), weighted median, weighted model, and simple model. Among them we focused on the sensitivity analysis results of inverse variance weighted (IVW) meta-analyses and MR Egger regressions, where IVW corresponds to weighted regressions of the effect of exposure on the outcome with an intercept restriction of zero, while the estimates of the MR-Egger method are relatively robust to the presence of pleiotropy[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the absence of horizontal pleiotropy, IVW-MR estimation is the most preferred choice for sensitivity analyses. Therefore for sensitivity analyses of SNPs, heterogeneity between SNPs included in the analysis was assessed using intercepts from IVW and MR Egger methods and quantified using Cochrane's Q-test. In addition, the MR-PRESSO method was used to assess and correct for overall horizontal pleiotropy, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates that there was no heterogeneity in the included IVs and the study's results do not need to take into account the effects caused by heterogeneity. Finally, leave-one-out sensitivity analyses were conducted to prevent potentially strong-influence SNPs from affecting the reliability and stability of causal effect estimates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eReverse Mendelian Randomization Analysis\u003c/h2\u003e \u003cp\u003eWe performed additional reverse MR analyses to explore reverse causality. Significant reverse MR analyses indicated reverse causality from AIDS (as exposure) to microbiota characteristics (as outcome). The reverse MR analysis procedure was the same as the MR analysis procedure described above.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eInstrumental Variables Selection\u003c/h2\u003e\n\u003cp\u003eAfter the instrumental variable selection process on the exposure data, 42 genus-associated SNPs were selected (genome-wide statistical significance threshold, P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5), and 12 SNPs were finally obtained after removing the linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, kb\u0026thinsp;=\u0026thinsp;10000). Meanwhile, for further analyses, we also collected other important information on the target SNPs including effector allele, other alleles, \u0026beta;, SE, and \u003cem\u003eP\u003c/em\u003e value. Finally, the F-statistics of the selected SNPs all exceeded 10, indicating that there was no weak instrumental variable bias in our study(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eThe impact of the gut microbiota on AIDS\u003c/h2\u003e\n\u003cp\u003eBased on the IVW method, 8 genera of bacterial taxa were found to predict a correlation between gut microbiota and the risk of AIDS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). MR analysis shows that Subdoligaranulum increases the risk of AIDS (OR\u0026thinsp;=\u0026thinsp;4 012; 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;1.783\u0026ndash;9.027; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.90E -04), the weighted median method also obtained similar results (OR\u0026thinsp;=\u0026thinsp;4.235; 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;1.366\u0026ndash;13.132; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.20E-02). Victivallis increased the risk of AIDS (OR\u0026thinsp;=\u0026thinsp;1.605; 95% CI\u0026thinsp;=\u0026thinsp;1.012\u0026ndash;2.547; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.40E-02), which was further confirmed by the weighted median method (OR\u0026thinsp;=\u0026thinsp;1.787; 95% CI\u0026thinsp;=\u0026thinsp;1.002\u0026ndash;3.186; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.90E-02). Ruminococcaceae_UCG-005 also increased the risk of HIV infection (OR\u0026thinsp;=\u0026thinsp;2.051; 95% CI\u0026thinsp;=\u0026thinsp;1.048\u0026ndash;4.011; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.60E-02). In contrast, genetically predicted Eggerthella (OR\u0026thinsp;=\u0026thinsp;0.477; 95% CI\u0026thinsp;=\u0026thinsp;0.283\u0026ndash;0.805; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.50E-03), Anaerotruncus (OR\u0026thinsp;=\u0026thinsp;0.434; 95% CI\u0026thinsp;=\u0026thinsp;0.197\u0026ndash;0.954), Methanobrevibacter (OR\u0026thinsp;=\u0026thinsp;0. 509 ; 95% CI\u0026thinsp;=\u0026thinsp;0.265\u0026ndash;0.980; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.30E-02), Clostridiumsensustricto1 (OR\u0026thinsp;=\u0026thinsp;0.424; 95% CI\u0026thinsp;=\u0026thinsp;0.182\u0026ndash;0.988; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.70E-02), and Coprococcus2 (OR\u0026thinsp;=\u0026thinsp;0.377; 95% CI\u0026thinsp;=\u0026thinsp;0.159\u0026ndash;0.894; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.70E-02) reduced the risk of AIDS (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Detailed statistics of the gut microbiota at all taxonomic levels are shown in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eSensitivity analyses\u003c/h2\u003e\n\u003cp\u003eCochran Q statistic for Subdoligranulum (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.544), Eggerthella (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.800), Coprococcus2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.610), Ruminococcaceae_UCG-005 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.568). Anaerotruncus (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.723), Methanobrevibacter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.298), Victivallis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.302), and Clostridiumsensustricto1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.483) Cochran's Q illustrates that the included IVs were not heterogeneous and had high reliability, and the results of the study did not need to take into account the effects caused by heterogeneity (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). MR-PRESSO had no significant effect on the quality of Subdoligranulum (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.541), Eggerthella (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.808), Coprococcus2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.632), Ruminococcaceae_UCG-005 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.592), Anaerotruncus (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.729), Metacercaria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.729), and Metacercaria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.483), Methanobrevibacter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36), Victivallis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32), and Clostridiumsensustricto1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.509) test analyses did not reveal significant horizontal pleiotropy between the instrumental variables and the results (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition to this, we conducted a leave-one-out sensitivity analysis using the IVW method to observe whether the results changed after removing each SNP. The results showed that the results did not change much after progressively removing a particular SNP. No SNP had a disproportionate effect on the total estimate (Figure. 3). Scatter plots showed the magnitude of the effect of gut microbiota SNPs on the estimated HIV risk (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on the results of the reverse MR analyses, there was no evidence of a significant causal effect of AIDS on gut-to-gut microbiota. Also, there was no significant heterogeneity and horizontal pleiotropy in the sensitivity analyses regarding the AIDS instrumental variables (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Detailed information about the IVs used in the reverse MR analysis is shown in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs far as we know, this two-sample MR study is the first to examine the causal association between gut microbiota and people living with HIV by using a publicly available genetic database of European populations. Our findings suggest that Subdoligranulum, Eggerthella, Coprococcus2, Ruminococcaceae_UCG-005, Anaerotruncus, Methanobrevibacter, Victivallis, and Clostridiumsensustricto1 microbial genera are causally associated with AIDS, validating the findings of many previous studies with the new identification of gut microbial genera associated with the risk of developing AIDS. This enhances the understanding of the role of gut microbiota in the pathogenesis of AIDS and provides new insights into the prevention and diagnosis of AIDS.\u003c/p\u003e \u003cp\u003eAs an important route of HIV transmission and a major site of early replication, alterations in the mucosal microbiota environment of the gastrointestinal tract may be closely related to the development of AIDS[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies on the gut microbiota and the risk of AIDS have a long history, and an increase in potentially pathogenic Aspergillus and Prevotella spp. and a decrease in commensal organisms such as Mycobacterium avium and Mycobacterium thicketi have been reported in people living with HIV [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the strength of evidence from such cross-sectional studies may be limited by diet, age, diet, geographic location, and multiple other confounding factors[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Genetic variation in humans is present at birth and remains stable throughout the life cycle. In the Mendelian randomization framework, genotypes (determined by genetic variation) are randomly assigned to offspring during meiosis, making them largely independent of confounding factors that typically plague observational epidemiological studies in large-scale populations[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, germline genetic variations (i.e. heritable variations) are temporarily fixed during conception and will not change due to any outcome or disease, thus ruling out the possibility of reverse causality. In addition, due to the improvement of modern genotyping technology, the measurement error and systematic misclassification of genetic data are usually low. Furthermore, Mendelian randomization can be considered similar to \"natural randomized controlled trials\". Therefore, associations obtained by MR methods are less affected by confounding factors. Notably, it has been shown that populations have limited changes in gut microbiota components during the acute phase of HIV infection and that specific gut microbial changes associated with HIV infection may not be apparent until the chronic phase, demonstrating that the observed differences in gut microbiota may pre-exist[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This gives reason to believe that changes in the gut microbiota may influence the course of the AIDS development. Thus, in the present study, we used human genus-level gut flora sequencing data as an exposure factor to assess its impact on the outcome of AIDS.\u003c/p\u003e \u003cp\u003eAs one of the gut microorganisms with the ability to produce butyrate[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Subdoligleagum is strongly associated with circulating CD4\u0026thinsp;+\u0026thinsp;T cell counts and T cell immune activation. Enrichment of this bacterium may lead to persistent CD4\u0026thinsp;+\u0026thinsp;T-cell depletion after HIV infection and immune unresponsiveness due to poor CD4\u0026thinsp;+\u0026thinsp;T-cell reconstitution even after effective antiretroviral therapy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, Subdoligleagum may be a risk factor for HIV infection, but the underlying mechanisms remain to be elucidated by further studies. The advent of antiretroviral therapy, currently the primary option for HIV treatment, has significantly improved the incidence of poor prognosis in people living with HIV[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, with the onset of physiologic aging, People living with HIV (PLWH) experience persistent low-grade inflammation and increased risk of other comorbidities[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This suggests that the gut microbiota may be affected by a combination of infection and age[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In a study comparing bacterial profiles in the feces of elderly PLWH with those of HIV-uninfected older adults (controls), the abundance of Eggertella spp. was lower in PLWH versus controls. Villanueva et al. also found that compared with uninfected controls, patients with PLWH treated with protease inhibitors in combination with nucleoside/tide reverse transcriptase inhibitors (NRTI) had a higher abundance of Eggertella was higher, but there was no difference in patients treated with other NRTI combinations[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Whereas our study demonstrated that Eggertella may be a protective factor in reducing the risk of AIDS, early intervention against this genus may be a target for improving persistent inflammation and comorbidities in late-stage PLWH. One study concluded that a reduction in Coprococcus 2, a member of the obesogenic microbiota, increased the risk of metabolic syndrome twofold in HIV-infected individuals[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Petersen et al.\u0026rsquo;s study [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]demonstrated that the growth of Vibrio vulnificus and the reduction of several species of Clostridium were sufficient to trigger fat accumulation and metabolic syndrome in immunodeficient mice, suggesting that Coprococcus 2 may be able to play a protective role by reducing metabolic risk in immunodeficient patients. Moreover, Cohort studies on the early post-HIV infection period have shown a reduced abundance of the Ruminococceae family[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This seems to be inconsistent with the finding of Ruminococcaceae_UCG-005 as an exposure risk factor for AIDS in this study. It is known from the current study that Ruminococcus spp. produce short-chain fatty acids (SCFA) through carbohydrate fermentation[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], but the role of SCFA in inflammation has not been clarified, and it may play multiple roles in different environmental conditions and cell types. The prevailing view is that SCFA is anti-inflammatory, but when the intestinal epithelium is infected or damaged by bacteria, SCFA may be pro-inflammatory, which may partly explain the discrepancy in the results of different studies on the role of these bacteria in inflammation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Charlene et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]in their assessment of the interrelationships between HIV and iron status and gut microbiota composition in non-school-aged children in South Africa found a lower relative abundance of the butyrate-producing genera Anaerostipes and Anaerotruncus in iron deficiency as compared to iron-sufficient children, which is in agreement with our findings.\u003c/p\u003e \u003cp\u003eMethanobrevibacter is a dominant archaeon commonly found in healthy populations, and its main species Mycobacterium smegmatis, has been well studied, its main role is to remove dihydrogen from the host intestinal environment and to promote the production of acetate, butyrate, and ATP[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It has been associated with obesity, colorectal cancer, anorexia, inflammatory bowel disease, irritable bowel syndrome, etc[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Simin et al.[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] suggest that this bacterium may contribute to the cognitive deficits that occur in PLWH and that it exerts a protective effect through immune activation causing chronic inflammation. An observational study reporting a significant enrichment of microbiota composition in HIV-exposed seronegative individuals (HESN) with Victivalis (p\u0026thinsp;=\u0026thinsp;0.0029) compared to healthy controls and those with progressed HIV infection suggests a role in modulating the risk of viral infection and controlling the extent of infection[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This is in contradiction with our findings, but considering that the genus Victoralis is also a known producer of SCFA[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and that the explanation for its protective effects stems from the anti-inflammatory, anti-tumor, and antimicrobial effects of SCFA, as well as its ability to maintain intestinal integrity and immune homeostasis[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Considering the variable role of SCFA and the specificity of the HESN population, it is reasonable to further explore the impact Victivallis plays in HIV infection. Another SCFA-producing genus[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], Clostridium sensustricto1 was identified by us as a protective factor for reducing AIDS risk.\u003c/p\u003e \u003cp\u003eThere are a couple of redeeming features of this study. First, the interference of confounding factors on causality was minimized by the stability of SNPs. Second, the research data used were the largest, most comprehensive, and widely selected to ensure the reliability of the results. Thirdly, this study used the classical two-sample MR analysis method, which was paired with a variety of sensitivity checks such as MR-PRESSO and MR-Egger regression intercept terms. Finally, inverse causality analysis inference was also performed to provide accurate quantitative estimates of causality. Our findings suggest that enhanced surveillance of the gut microbiota in specific populations may help reduce the risk of AIDS incidence.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations. First, the majority of individuals in the GWAS pooled data used in this study were European populations, and the conclusions drawn from this need to be validated on other races. Second, We only analyzed bacterial taxa at the genus level, which is mainly a reference to the selection of other studies[\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and it is necessary to further explore the causal relationship between the gut microbiota and the risk of HIV disease at the level of a wider range of species taxa to obtain more complete conclusions. To obtain more SNPs as instrumental variables to fulfill the requirements of sensitivity analysis and horizontal pleiotropy testing, we did not use the traditional GWAS significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;8) to screen for SNPs. For this reason, there may be a lack of statistical efficacy in the analysis results, but we subsequently utilized multiple correction methods to limit the occurrence of false negatives as much as possible. Third, considering the contradictions in the conclusions we drew in the SCFA-producing genus species (Ruminococcaceae_UCG-005, Victivallis, and Clostridiumsensustricto1 ), taking into account the fact that SCFA may play different roles in different scenarios, and the role of SCFA in signaling due to the different ligand-receptor pair combinations during signaling may result in the induction of different signaling cascades in different immune cells[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, more detailed studies in this area are needed to clarify the mechanism of action and species-specific effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study demonstrated the causal relationship between gut microbiota and AIDS through MR analysis, revealed the influence of specific gut microbiota taxa on the risk of AIDS, and, based on a large number of previous observational studies on gut microbiota changes after HIV infection, put forward the viewpoint that alterations in gut microbiota affect the risk of AIDS, which provides new evidence and research ideas for the prevention of the disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data involving human participants in this study are published data for which informed consent has been obtained from the participants. Written informed consent was not required for participation in the studies, in accordance with national legislative and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grant from the National Natural Science Foundation of China (81772043, 81971879).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study were provided by the MiBioGen consortium with the Open Gwas website. We thank them for their contribution to the study and the participants of the corresponding study, without whom this work would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003e\"UN Joint Programme on HIV/AIDS. \"[Online]\u003c/em\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unaids.org/en/resources/fact-sheet\u003c/span\u003e\u003cspan address=\"https://www.unaids.org/en/resources/fact-sheet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeeks, S.G., et al., \u003cem\u003eHIV infection\u003c/em\u003e. Nat Rev Dis Primers, 2015. 1: p. 15035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeazey, R.S., et al., \u003cem\u003eGastrointestinal tract as a major site of CD4\u0026thinsp;+\u0026thinsp;T cell depletion and viral replication in SIV infection\u003c/em\u003e. Science, 1998. 280(5362): p. 427\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChege, D., et al., \u003cem\u003eSigmoid Th17 populations, the HIV latent reservoir, and microbial translocation in men on long-term antiretroviral therapy\u003c/em\u003e. Aids, 2011. 25(6): p. 741\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrenchley, J.M., et al., \u003cem\u003eMicrobial translocation is a cause of systemic immune activation in chronic HIV infection\u003c/em\u003e. Nat Med, 2006. 12(12): p. 1365\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, C.J., et al., \u003cem\u003eMucosal Th17 cell function is altered during HIV infection and is an independent predictor of systemic immune activation\u003c/em\u003e. J Immunol, 2013. 191(5): p. 2164\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDillon, S.M., et al., \u003cem\u003eAn altered intestinal mucosal microbiome in HIV-1 infection is associated with mucosal and systemic immune activation and endotoxemia\u003c/em\u003e. Mucosal Immunol, 2014. 7(4): p. 983\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLozupone, C.A., et al., \u003cem\u003eAlterations in the gut microbiota associated with HIV-1 infection\u003c/em\u003e. Cell Host Microbe, 2013. 14(3): p. 329\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcHardy, I.H., et al., \u003cem\u003eHIV Infection is associated with compositional and functional shifts in the rectal mucosal microbiota\u003c/em\u003e. Microbiome, 2013. 1(1): p. 26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulcher, J.A., et al., \u003cem\u003eGut dysbiosis and inflammatory blood markers precede HIV with limited changes after early seroconversion\u003c/em\u003e. EBioMedicine, 2022. 84: p. 104286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocafort, M., et al., \u003cem\u003eEvolution of the gut microbiome following acute HIV-1 infection\u003c/em\u003e. Microbiome, 2019. 7(1): p. 73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y., et al., \u003cem\u003eSignature changes in gut microbiome are associated with increased susceptibility to HIV-1 infection in MSM\u003c/em\u003e. Microbiome, 2021. 9(1): p. 237.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandley, S.A., et al., \u003cem\u003eSIV Infection-Mediated Changes in Gastrointestinal Bacterial Microbiome and Virome Are Associated with Immunodeficiency and Prevented by Vaccination\u003c/em\u003e. Cell Host Microbe, 2016. 19(3): p. 323\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlase, Z., et al., \u003cem\u003eDysbiotic bacteria translocate in progressive SIV infection\u003c/em\u003e. Mucosal Immunol, 2015. 8(5): p. 1009\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYatsunenko, T., et al., \u003cem\u003eHuman gut microbiome viewed across age and geography\u003c/em\u003e. Nature, 2012. 486(7402): p. 222\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVujkovic-Cvijin, I., et al., \u003cem\u003eHost variables confound gut microbiota studies of human disease\u003c/em\u003e. Nature, 2020. 587(7834): p. 448\u0026ndash;454.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, T.S., \u003cem\u003eMendelian randomization: methods for causal inference using genetic variants: CRC Press\u003c/em\u003e;. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies, N.M., M.V. Holmes, and G. Davey Smith, \u003cem\u003eReading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians\u003c/em\u003e. Bmj, 2018. 362: p. k601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurilshikov, A., et al., \u003cem\u003eLarge-scale association analyses identify host factors influencing human gut microbiome composition\u003c/em\u003e. Nat Genet, 2021. 53(2): p. 156\u0026ndash;165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003e\u0026ldquo;Human immunodeficiency virus [HIV] disease gwas datasets\"[online]\u003c/em\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_HIV/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_HIV/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi, J.J., et al., \u003cem\u003eGut Microbiota and Psychiatric Disorders: A Two-Sample Mendelian Randomization Study\u003c/em\u003e. Front Microbiol, 2021. 12: p. 737197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, J., et al., \u003cem\u003eThe causal links between gut microbiota and COVID-19: A Mendelian randomization study\u003c/em\u003e. J Med Virol, 2023. 95(5): p. e28784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, M., et al., \u003cem\u003eNo Evidence of a Genetic Causal Relationship between Ankylosing Spondylitis and Gut Microbiota: A Two-Sample Mendelian Randomization Study\u003c/em\u003e. Nutrients, 2023. 15(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, M., et al., \u003cem\u003eCausal effects of gut microbiota on the risk of chronic kidney disease: a Mendelian randomization study\u003c/em\u003e. Front Cell Infect Microbiol, 2023. 13: p. 1142140.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGagnon, E., et al., \u003cem\u003eImpact of the gut microbiota and associated metabolites on cardiometabolic traits, chronic diseases and human longevity: a Mendelian randomization study\u003c/em\u003e. J Transl Med, 2023. 21(1): p. 60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess, S. and S.G. Thompson, \u003cem\u003eAvoiding bias from weak instruments in Mendelian randomization studies\u003c/em\u003e. Int J Epidemiol, 2011. 40(3): p. 755\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., G. Davey Smith, and S. Burgess, \u003cem\u003eMendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression\u003c/em\u003e. Int J Epidemiol, 2015. 44(2): p. 512\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrendergast, A., et al., \u003cem\u003eHIV-1 infection is characterized by profound depletion of CD161\u0026thinsp;+\u0026thinsp;Th17 cells and gradual decline in regulatory T cells\u003c/em\u003e. Aids, 2010. 24(4): p. 491\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVujkovic-Cvijin, I., et al., \u003cem\u003eDysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism\u003c/em\u003e. Sci Transl Med, 2013. 5(193): p. 193ra91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutlu, E.A., et al., \u003cem\u003eA compositional look at the human gastrointestinal microbiome and immune activation parameters in HIV infected subjects\u003c/em\u003e. PLoS Pathog, 2014. 10(2): p. e1003829.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekula, P., et al., \u003cem\u003eMendelian Randomization as an Approach to Assess Causality Using Observational Data\u003c/em\u003e. J Am Soc Nephrol, 2016. 27(11): p. 3253\u0026ndash;3265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEeckhaut, V., et al., \u003cem\u003eButyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum\u003c/em\u003e. Microb Biotechnol, 2011. 4(4): p. 503\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, W., et al., \u003cem\u003eAssociation Between Gut Microbiota and CD4 Recovery in HIV-1 Infected Patients\u003c/em\u003e. Front Microbiol, 2018. 9: p. 1451.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLundgren, J.D., et al., \u003cem\u003eInitiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection\u003c/em\u003e. N Engl J Med, 2015. 373(9): p. 795\u0026ndash;807.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimon, V., D.D. Ho, and Q. Abdool Karim, \u003cem\u003eHIV/AIDS epidemiology, pathogenesis, prevention, and treatment\u003c/em\u003e. Lancet, 2006. 368(9534): p. 489\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker, B.D. and M.S. Hirsch, \u003cem\u003eAntiretroviral therapy in early HIV infection\u003c/em\u003e. N Engl J Med, 2013. 368(3): p. 279\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeeks, S.G., R. Tracy, and D.C. Douek, \u003cem\u003eSystemic effects of inflammation on health during chronic HIV infection\u003c/em\u003e. Immunity, 2013. 39(4): p. 633\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuford, T.W., \u003cem\u003e(Dis)Trust your gut: the gut microbiome in age-related inflammation, health, and disease\u003c/em\u003e. Microbiome, 2017. 5(1): p. 80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZevin, A.S., et al., \u003cem\u003eMicrobial translocation and microbiome dysbiosis in HIV-associated immune activation\u003c/em\u003e. Curr Opin HIV AIDS, 2016. 11(2): p. 182\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J., et al., \u003cem\u003eAmong older adults, age-related changes in the stool microbiome differ by HIV-1 serostatus\u003c/em\u003e. EBioMedicine, 2019. 40: p. 583\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillanueva-Mill\u0026aacute;n, M.J., et al., \u003cem\u003eDifferential effects of antiretrovirals on microbial translocation and gut microbiota composition of HIV-infected patients\u003c/em\u003e. J Int AIDS Soc, 2017. 20(1): p. 21526.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelpi, M., et al., \u003cem\u003eImpact of Human Immunodeficiency Virus-Related Gut Microbiota Alterations on Metabolic Comorbid Conditions\u003c/em\u003e. Clin Infect Dis, 2020. 71(8): p. e359-e367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen, C., et al., \u003cem\u003eT cell-mediated regulation of the microbiota protects against obesity\u003c/em\u003e. Science, 2019. 365(6451).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZe, X., et al., \u003cem\u003eRuminococcus bromii is a keystone species for the degradation of resistant starch in the human colon\u003c/em\u003e. Isme j, 2012. 6(8): p. 1535\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinolo, M.A., et al., \u003cem\u003eRegulation of inflammation by short chain fatty acids\u003c/em\u003e. Nutrients, 2011. 3(10): p. 858\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrison, D.J. and T. Preston, \u003cem\u003eFormation of short chain fatty acids by the gut microbiota and their impact on human metabolism\u003c/em\u003e. Gut Microbes, 2016. 7(3): p. 189\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoosen, C., et al., \u003cem\u003eAssociations of HIV and iron status with gut microbiota composition, gut inflammation and gut integrity in South African school-age children: a two-way factorial case-control study\u003c/em\u003e. J Hum Nutr Diet, 2023. 36(3): p. 819\u0026ndash;832.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaci, N., et al., \u003cem\u003eArchaea and the human gut: new beginning of an old story\u003c/em\u003e. World J Gastroenterol, 2014. 20(43): p. 16062\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDridi, B., et al., \u003cem\u003eHigh prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the human gut using an improved DNA detection protocol\u003c/em\u003e. PLoS One, 2009. 4(9): p. e7063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamara, A., et al., \u003cem\u003eClinical evidence of the role of Methanobrevibacter smithii in severe acute malnutrition\u003c/em\u003e. Sci Rep, 2021. 11(1): p. 5426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua, S., et al., \u003cem\u003eGut Microbiota and Cognitive Function Among Women Living with HIV\u003c/em\u003e. J Alzheimers Dis, 2023. 95(3): p. 1147\u0026ndash;1161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopera, T.J., et al., \u003cem\u003eA specific structure and high richness characterize intestinal microbiota of HIV-exposed seronegative individuals\u003c/em\u003e. PLoS One, 2021. 16(12): p. e0260729.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoetendal, E.G., et al., \u003cem\u003eVictivallis vadensis gen. nov., sp. nov., a sugar-fermenting anaerobe from human faeces\u003c/em\u003e. Int J Syst Evol Microbiol, 2003. 53(Pt 1): p. 211\u0026ndash;215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuille, S., et al., \u003cem\u003eHuman gut bacteria as potent class I histone deacetylase inhibitors in vitro through production of butyric acid and valeric acid\u003c/em\u003e. PLoS One, 2018. 13(7): p. e0201073.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivera-Ch\u0026aacute;vez, F., et al., \u003cem\u003eDepletion of Butyrate-Producing Clostridia from the Gut Microbiota Drives an Aerobic Luminal Expansion of Salmonella\u003c/em\u003e. Cell Host Microbe, 2016. 19(4): p. 443\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnriquez, A.B., et al., \u003cem\u003eRegulation of Immune Homeostasis, Inflammation, and HIV Persistence by the Microbiome, Short-Chain Fatty Acids, and Bile Acids\u003c/em\u003e. Annu Rev Virol, 2023. 10(1): p. 397\u0026ndash;422.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acquired Immune Deficiency Syndrome, Gut microbiota, Causal inference, Mendelian randomization study","lastPublishedDoi":"10.21203/rs.3.rs-4493955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4493955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEmerging evidence suggests that changes in the composition of the gut microbiota may not only be a consequence of AIDS but may also influence the risk of disease. However, it is not clear that these associations point to the certainty of causality.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo reveal the causal relationship between gut microbiota and AIDS, we performed a two-sample Mendelian randomization (MR) analysis.\u003c/p\u003e\u003ch2\u003eMaterials And Methods\u003c/h2\u003e \u003cp\u003eWe evaluated summary statistics of gut microbiota and HIV infection disease from published genome-wide association studies (GWAS). A two-sample MR analysis was performed to identify HIV-causing bacterial taxa in the samples based on inverse variance weighting (ivw) results. Sensitivity analyses were performed to verify the stability of the results. Finally, an inverse MR analysis was performed to assess the possibility of reverse causality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCombining the results of MR analysis and sensitivity analysis, we identified eight pathogenic bacterial genera: Subdoligaranulum (OR\u0026thinsp;=\u0026thinsp;4.012,95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;1.783\u0026ndash;9.027, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.90E-04), Victivallis(OR\u0026thinsp;=\u0026thinsp;1.605,95% CI\u0026thinsp;=\u0026thinsp;1.012\u0026ndash;2.547, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.40E-02), and Ruminococcaceae_UCG-005 (OR\u0026thinsp;=\u0026thinsp;2.051, 95% CI\u0026thinsp;=\u0026thinsp;1.048\u0026ndash;4.011, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.60E-02) increased the risk of HIV infection. In contrast, genetically predicted Eggerthella (OR\u0026thinsp;=\u0026thinsp;0.477, 95%CI\u0026thinsp;=\u0026thinsp;0.283\u0026ndash;0.805, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.50E-03), Anaerotruncus (OR\u0026thinsp;=\u0026thinsp;0.434, 95% CI\u0026thinsp;=\u0026thinsp;0.197\u0026ndash;0.954,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.8E-02), Methanobrevibacter (OR\u0026thinsp;=\u0026thinsp;0. 509 ; 95% CI\u0026thinsp;=\u0026thinsp;0. 265\u0026thinsp;\u0026minus;\u0026thinsp;0.980; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.30E-02), Clostridiumsensustricto1 (OR\u0026thinsp;=\u0026thinsp;0.424, 95% CI\u0026thinsp;=\u0026thinsp;0.182\u0026ndash;0.988, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.70E-02) and Coprococcus2 (OR\u0026thinsp;=\u0026thinsp;0.377, 95% CI\u0026thinsp;=\u0026thinsp;0.159\u0026ndash;0.894, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.70E-02) reduced the risk of HIV infection. Further sensitivity analyses verified the robustness of the above associations. Reverse MR analysis showed no evidence of reverse causality between HIV infection and the eight genera mentioned above.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrates that Subdoligaranulum, Victivallis, Ruminococcaceae_UCG-005,Eggerthella, Clostridiumsensustricto1. Coprococcus2 and AIDS are causally linked, thus providing new insights into the mechanisms underlying the onset of gut microbiota-mediated HIV infection.\u003c/p\u003e","manuscriptTitle":"Causal Relationship Between Gut Microbiota and Acquired Immune Deficiency Syndrome: A Two-Sample Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 07:22:56","doi":"10.21203/rs.3.rs-4493955/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c024ca3-e9bb-411e-946f-04f8d8533d9a","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-28T06:14:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 07:22:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4493955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4493955","identity":"rs-4493955","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}