Gut microbiota mediating the effect of chronic kidney disease on cognitive dysfunction: a mendelian randomization study

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Methods This study aimed to explore the potential genetic causality between CKD and cognitive dysfunction using two-sample Mendelian randomization. Furthermore, mediated Mendelian randomization was used to investigate potential genetic mechanisms. Results Our study utilizes a two-step Mendelian randomization approach to establish a causal link between chronic kidney disease and cognitive dysfunction, with the gut microbiome playing a pivotal mediating role. The study underscores the intricate relationship between renal function markers—particularly creatinine, which paradoxically correlates positively with cognitive performance—and cognitive health, while also pointing to the modifiable nature of specific gut microbes, such as the Eubacterium fissicatena group, as potential influencers of cognitive decline. Additionally, methanogens' presence in diseases and their dual role in inflammation suggest a complex impact on health that warrants deeper investigation. Conclusions The study found a causal link between CKD and cognitive dysfunction, with the gut microbiome acting as a mediator. Chronic kidney disease Gut microbiota Cognitive dysfunction mediation Mendelian randomization creatinine methanogens Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Kidney function continuously declines as a chronic condition known as chronic kidney disease (CKD) [ 1 ] . One of the major worldwide health issues now being faced by the aging global population is the rising prevalence of CKD, which has been on the rise annually [ 2 ] . According to estimates, 10% of people worldwide suffer from CKD [ 1 , 2 ] , and the condition is linked to a significant illness burden and mortality risk [ 2 , 3 ] . Cognitive dysfunction is characterized as diminished or impaired mental and/or intellectual performance, which includes deficits in memory, executive functioning, attention, language, and visuospatial function [ 4 ] . This impairment can have a significant impact on an individual's daily routine, work, and social activities, lowering their quality of life. In recent years, an increasing number of studies have focused on the relationship between CKD and cognitive dysfunction. According to data from the United States and Denmark, the prevalence of cognitive dysfunction among dialysis patients is between 16 and 38% [ 5 ] , which is more than three to five times that of general groups with normal renal function [ 6 ] . Furthermore, patients with CKD and their families report that their memory deteriorates, as do their language abilities, numeracy, understanding, and judgement, all of which have a substantial impact on their quality of life in severe instances [ 7 , 8 ] . Some investigations have demonstrated that cognitive dysfunction has a strong positive link with CKD and prognostic markers such as long-term all-cause mortality in dialysis patients [ 3 ] . Domestic and international studies on the relationship between cognitive dysfunction and CKD are currently in the exploratory stage, and there is still debate about the causal relationship between CKD and cognitive dysfunction. Some studies indicate that CKD may cause cognitive dysfunction [ 9 – 11 ] , while others imply that cognitive dysfunction may be caused by other factors such as age, education level, vascular disease [ 12 ] , hypertension [ 13 ] , and diabetes mellitus [ 14 , 15 ] . However, previous studies have revealed limitations, including insufficient statistical robustness and some shortcomings in making causal inferences due to small population sizes, different interpretations of the findings, and the presence of confounders such as socio-cultural demographic characteristics and pre-existing cerebrovascular events. In addition, the particular mechanisms underlying cognitive dysfunction caused by CKD have yet to be fully understood, requiring further research. Declining kidney function results in increased blood creatinine and urea, causing protein loss and heightened reactive oxygen species (ROS), which also modifies the gut microbiota composition [ 16 , 17 ] . Previous research indicates that gut microbiota alterations can affect cognitive function via immune system modulation, neuroactive metabolite production, and gut barrier interaction [ 18 – 21 ] . Given the established connection between the gut microbiota and the brain, it is plausible that the gut microbiota may also be a critical link between renal function and cognitive health. Mendelian randomization (MR) is a method that uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for causal inference [ 22 – 24 ] . Large-scale genome-wide association studies (GWAS) have been used to assess risk factors for cognitive dysfunction, exploring causal associations between exposure factors like hypertension, inflammatory bowel disease, and coronary heart disease. Additionally, GWAS studies have identified SNP loci significantly associated with CKD [ 25 – 27 ] , providing a basis for MR studies to investigate causal associations between CKD and various diseases, including cognitive dysfunction. This study aimed to explore the potential genetic causality between CKD and cognitive dysfunction using two-sample Mendelian randomization. Furthermore, mediated Mendelian randomization was used to investigate potential genetic mechanisms. This approach could provide new insights into the mechanisms linking renal health with cognitive function alterations, possibly through gut microbiota modulation. It offers a fresh viewpoint on the treatment and prognosis of chronic kidney disease, as well as a new target for the prevention and therapy of cognitive impairment. 2. Methods 2.1 Study Design This study had a two-part design, as shown in Fig. 1 . To explore the causal link between CKD and cognitive dysfunction, we used a two-sample MR. Then, we employed a two-step MR methodology (also known as mediated Mendelian randomization) to identify the gut microbiota's mediating role in the association between cognitive dysfunction and CKD. The study analyzed publicly accessible summary information on CKD, cognitive dysfunction, and gut microbiota (GM) from prior research or consortiums. There was no need to reapply to the institutional review board (IRB) for permission because each of these studies had already received clearance from its IRB. 2.2 Data Source The characteristics of the corresponding GWAS data sources are given in Fig. 1 . The study used seven indexes to predict CKD exposure, including CKD, GFR, creatinine, creatinine in urine, urinary albumin excretion, and cystatin C. The statistics were sourced from the IEU open GWAS database ( https://gwas.mrcieu.ac.uk/ ) as GWAS-IDs of "ebi-a-GCST003374" for CKD, "ebi-a-GCST003372" for GFR, "met-d-Creatinine" for creatinine, "ukb-a-333" for creatinine in urine, "ebi-a-GCST006586" for urinary albumin excretion, and "ukb-d-30720_irnt" for cystatin C. The sample size included 12,385 cases and 104,780 controls for CKD, 133,413 European participants for GFR, 110,058 for creatinine, 327,525 for urine creatinine, 382,500 for urinary albumin excretion, and 24,810 for cystatin C. The study examined genome-wide associations of cognitive outcome levels among European participants, focusing on eight indexes. The statistics were sourced from the IEU open GWAS database, examining cognitive performance (ebi-a-GCST006572), cognitive functions (ieu-b-4838), mean time to correctly identify matches (ukb-b-16287), number of fluid intelligence questions attempted within time limit (ukb-b-2988), number of symbol digit matches made correctly (ukb-b-15625), prospective memory result (ukb-b-4282), round of numeric memory test (ukb-b-16872), and time to answer (ukb-b-14360). Detailed information is provided in the Additional File, Table S1 . The gut microbiota data was sourced from the latest GWAS summary data, analyzed by the MiBioGen consortium, which included 211 gut microbiota taxa from 18,340 individuals [ 28 ] . The study included 78% of Europeans and used only taxa present in more than 10% of samples to identify genetic loci affecting relative abundance, resulting in 211 taxa, including 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. 2.3 Instrumental Variables Selection The cornerstone of MR studies lies in the prudent selection of IVs. A judicious choice of IVs is fundamental to securing unbiased estimates of causal relationships. SNPs were screened based on the following criteria: (1) A P value < 5×10 − 8 indicated a significant connection between SNPs and exposure. For GWAS studies with small sample numbers, the criterion was reduced to P < 1×10 − 5 . (2) Subsequently, using the 1000 Genomes reference panel, independent SNPs were clumped at a stringent linkage disequilibrium (LD) threshold of r 2 < 0.001 [ 29 ] . In cases where no shared SNPs existed between the outcome and exposure, proxies derived from the European reference panel 1000 Genomes (r 2 ≥ 0.8) were incorporated. (3) SNPs with an effect allele frequency exceeding 0.01 were included, while those with an F-statistic below 10 were excluded, as a measure to ensure the robustness of the instrumental variables and to mitigate the risk of weak instrumental bias [ 30 ] . (4) PhenoScanner V2 ( http://www.phenoscanner.medschl.cam.ac.uk/ ) was utilized to scrutinize the collected SNPs for potential confounders and bypassing factors, including but not limited to age, gender, race, and other illnesses [ 31 ] . 2.4 MR analysis All MR analyses were performed in R (version 4.3.1) software, using the “TwoSampleMR” (version 0.5.7) ( https://github.com/MRCIEU/TwoSampleMR ) [ 32 ] and “MR-PRESSO” (version 1.0) ( https://github.com/rondolab/MR-PRESSO ) [ 33 ] packages. 2.4.1 Primary analysis The inverse-variance-weighted (IVW) method, a conventional approach in Mendelian randomization, was applied for estimating effects. Outcomes were presented as odds ratios (OR) with 95% confidence intervals (CI). Significance was defined as P < 0.05. Statistical significance was set at a nominal threshold of P < 0.05. A random-effects model was employed to account for heterogeneity, which was quantified using Cochran's Q test [ 34 ] . The IVW method, despite providing the most accurate effect estimates, overlooked null instruments and pleiotropy. To address these limitations, four alternative approaches were utilized: the weighted median estimation model (WME), the weighted mode approach (WM), the MR-Egger regression model, and the simple mode (SM), each assuming different levels of horizontal pleiotropy [ 32 , 35 ] . To ensure a robust causal inference from the MR estimate and sensitivity analysis methods previously outlined, the following criteria were deemed essential: (1) Consistent directional findings across the five methods were required for the MR estimate and sensitivity analysis. (2) The MR-Egger intercept test must indicate the absence of significant horizontal pleiotropy. 2.4.2 Mediation analysis The exploration of mediating effects generates three parameters: the direct effect of exposure on the outcome (Fig. 1 , XY), the effect of exposure on the mediating variable (Fig. 1 , XZ), and the effect of the mediating variable on the outcome (Fig. 1 , ZY). If all three parameters are significant, it indicates a causal association from exposure to outcome, partially mediated by the mediator variable. If XY is significant but at least one of XZ and ZY is not significant, no mediation occurs. 3. Results 3.1 Genetic causality and correlation between CKD and cognitive dysfunction 3.1.1 Selection of IVs related to CKD Six carefully selected sets of IVs were meticulously tailored to accurately reflect the spectrum of CKD, encompassing essential biomarkers such as GFR, creatinine, creatinine in urine, urinary albumin excretion, and cystatin C. We conducted a comprehensive evaluation of the F-statistic values for each IV and for every set of IVs, confirming their robustness as strong IVs with F-statistics exceeding the threshold of 10. Following a series of quality control protocols, the lists of included SNPs were presented in the Additional File, Table S2 . 3.1.2 Two-sample Mendelian randomization analysis After ascertaining that all IVs were valid under existing conditions, we performed a two-sample MR analysis for the relationship between CKD and cognitive dysfunction. In our application of the IVW method, several significant associations were observed between several renal and cognitive biomarkers, which are detailed as follows: Creatinine was associated with a decreased likelihood of attempting more fluid intelligence questions within the time limit, with an odds ratio (OR) of 0.911 (95% CI [0.839, 0.989], P = 0.027). The overall cognitive performance showed a similar trend with creatinine intake, yielding an OR of 0.962 (95% CI [0.931, 0.993], P = 0.018). Interestingly, urinary creatinine levels were positively associated with cognitive performance, with an OR of 1.194 (95% CI [1.025, 1.391], P = 0.023). Urinary albumin excretion demonstrated an inverse relationship with cognitive performance for both the number of symbol-digit matches made correctly (OR = 0.861, 95% CI [0.754, 0.984], P = 0.028) and the round of the numeric memory test (OR = 0.860, 95% CI [0.742, 0.997], P = 0.046). Serum cystatin C levels were positively correlated with the number of symbol-digit matches made correctly, with an OR of 1.029 (95% CI [1.002, 1.056], P = 0.036). GFR also showed a positive association with the number of symbol-digit matches made correctly, as indicated by an OR of 1.234 (95% CI [1.001, 1.554], P = 0.049). These findings are also illustrated in Fig. 2 and elaborated upon in the Additional File, including Table S3 . Employing a variety of statistical methodologies, we observed consistent directional estimates between renal and cognitive biomarkers, thereby reinforcing the validity and robustness of our principal findings (Additional File, Table S3 ). However, the relationship between GFR and the number of symbol-digit matches made correctly presented a notable exception, with discordant directional estimates. To ensure the fidelity and reliability of the reported causal relationships and to be in line with best practices in Mendelian randomization studies, GFR was excluded from the primary outcome presentation in Fig. 2 . Scatter plots and forest plots consistently depicted the causal effects of chronic kidney disease (CKD) on cognitive dysfunction (Additional File, Fig. S1 and Fig. S2 ). No significant horizontal pleiotropy was detected. Furthermore, no significant heterogeneity was observed. The detailed data are presented in the Additional File, Table S4 . The leave-one-out analysis confirmed the stability of the causal effect, indicating that the impact of CKD on cognitive dysfunction is not significantly influenced by any single nucleotide polymorphism (SNP) (Additional File, Fig. S3 ). 3.2 The gut microbiota mediates the causal role of CKD on cognitive dysfunction In our MR study, we found a protective effect of creatinine on cognitive performance, with an odds ratio of 0.911 for fluid intelligence questions and 0.962 for overall cognitive performance. However, creatinine in urine had an OR of 1.194 for cognitive performance, suggesting a divergent mechanism. Previous research has indicated a relationship between creatinine levels and alterations in the gut microbiome. Building on these findings and given the established role of creatinine as an indicator of renal function, our forthcoming research will pivot to an in-depth investigation of creatinine. We conducted a mediation MR study to determine if the GM mediates the causal effects of CKD on cognitive dysfunction. 3.2.1 Selection of IVs related to GM From a total of 211 gut microbiota taxa, the study eventually identified 122,110 variation sites at five levels: phylum, class, order, family, and genus. To ensure the accuracy of the data, our study excluded 15 bacterial taxa from unknown families or genera, leaving 196. The median F-statistic was 21.0 (ranging from 14.6 to 88.4) for GM, and an F-statistic > 10 is considered sufficiently informative for MR analyses (Additional File, Table S5 ). 3.2.2 Mediation analysis We performed two-sample analyses to investigate the relationship between creatinine levels in both blood and urine and the gut microbiota (step 1 MR). The results of the IVW analysis are shown in Fig. 3 A, whereby the relationship between urine creatinine levels and seven distinct gut microbiota taxa is explained. Six of these taxa showed consistent causal effects. Notably, the class Methanobacteria, the order Methanobacteriales, the family Methanobacteriaceae, and three genera—Eubacterium rectale group, Howardella, and Rikenellaceae RC9 gut group—were found to be negatively correlated with creatinine levels. On the other hand, the genus Ruminococcaceae (UCG-004) showed a positive connection. Additionally, creatinine levels demonstrated significant associations with six other gut microbiota taxa, four of which indicated consistent causality. The genus Barnesiella and the family Peptococcaceae showed positive relationships. On the other hand, there were negative connections found with four genera: Terrisporobacter, Lachnoclostridium, Bilophila, and Eubacterium rectale group. Similarly, we conducted two-sample analyses to examine the relationship between gut microbiota and cognitive dysfunction (step 2 MR). We identified 10 gut microbiota taxa that exhibited causal effects on cognitive dysfunction. Specifically, three taxa were associated with an increased risk of impaired cognitive performance: the class Methanobacteria, the order Methanobacteriales, and the family Methanobacteriaceae. In contrast, two genera, Barnesiella and RuminococcaceaeUCG-004, were found to potentially decrease the risk of adverse prospective memory outcomes, while the genus Bilophila was associated with an increased risk for this cognitive domain. The IVW analysis depicted in Fig. 3 B revealed significant positive correlations between the genus Eubacterium brachygroup and Lachnoclostridium with the number of symbol-digit matches made correctly. However, the Eubacterium brachygroup showed a significant negative correlation with the round of numeric memory test performance. Additionally, the genus Howardella was positively correlated with time to answer, while Bilophila was associated with the mean time to correctly identify matches. The Rikenellaceae RC9 gut group demonstrated a positive correlation with the number of fluid intelligence questions attempted within the time limit, while the Eubacterium fissicatena group demonstrated a negative correlation. To further validate the robustness of our findings, we performed a series of sensitivity analyses, detailed in Additional File, Table S6 . The majority of these analyses yielded consistent results, albeit with broader confidence intervals. Cochran’s Q test results, all exceeding the threshold of 0.05, indicated no significant heterogeneity among the studies (Additional File, Table S4 ). The MR-PRESSO analysis confirmed the absence of influential outliers among SNPs. Furthermore, both the MR-Egger intercept test and the global test p-values showed no evidence of horizontal pleiotropy, reinforcing the credibility of our MR findings. Utilizing a two-step Mendelian randomization (MR) approach with the 'product of coefficients' method, we examined whether the gut microbiota mediates the relationship between urinary creatinine levels and cognitive performance. The mediation effect was determined by the ratio of the gut microbiota-mediated effect to the total effect of urinary creatinine on cognition. Our study revealed that the class Methanobacteria, order Methanobacteriales, and family Methanobacteriaceae partially moderate this association, with a mediation proportion of 7.97%, as shown in Fig. 4 . The critical role that the Eubacterium rectale group plays as a mediator in the causal pathway that links creatinine levels to the number of fluid intelligence questions attempted within the time limit, as shown in Fig. 4 , is highlighted by the mediation analysis. 4. Discussion The study used a two-sample MR method to explore the causal relationship between CKD and cognitive dysfunction. The MR results showed that this causal connection existed. Furthermore, we also discovered a link between CKD and cognitive dysfunction via the gut microbiome using a two-step MR study. Encouragingly, we found that some gut microbiota could mediate or moderate this relationship. The two-sample MR analysis revealed intriguing associations between renal biomarkers and cognitive performance. In some ways, the current study is similar to earlier research, but not completely. Previous studies have mixed results, with most comparing cognitive outcomes based on eGFR [ 36 – 39 ] . Due to discrepancies in the directional estimations of the link between GFR and cognitive function, GFR was excluded from our primary outcome presentation. Studies involving kidney damage markers (i.e., albuminuria or proteinuria) have been more consistent in reporting cognitive function associations [ 36 , 40 , 41 ] . This is in line with our observations that urinary albumin excretion demonstrated an inverse relationship with cognitive performance, indicating that albuminuria could be a potential risk factor for cognitive impairment. Furthermore, our study contributes to the emerging evidence that serum cystatin C may be a risk factor for cognitive decline. This finding is consistent with the hypothesis that inflammation and vascular damage, which are often associated with kidney disease, can have detrimental effects on cognitive function [ 42 , 43 ] . Conversely, the observation that high creatinine levels may improve memory and learning functions challenges conventional understanding [ 44 ] . Our study suggests a potential protective role of creatinine in cognitive processes, as indicated by the ORs for fluid intelligence questions and overall cognitive performance. The unexpected positive association between urinary creatinine levels and cognitive performance introduces a layer of complexity. This warrants further investigation, as it could indicate a more complex interplay between renal function markers and cognitive health than previously recognized. This divergence may reflect distinct metabolic pathways or the influence of other, as yet undefined, factors. The exploration of the GM mediating role in the causal pathway from CKD to cognitive dysfunction represents a significant advancement in our understanding of the complex interplay between renal health and cognitive function. Building upon previous research that implicated creatinine levels and alterations in the GM, our study introduces a two-step MR approach. This methodological choice allowed us to rigorously investigate the potential mediating effects of the GM in a manner that leverages the strengths of genetic instrumental variables to infer causality. Creatinine, a metabolic waste product, accumulates in the blood as kidney function declines [ 45 ] . Healthy kidneys easily remove creatinine from the circulation via urine; however, impaired kidney function can result in lower creatinine excretion in the urine [ 46 ] . The elevated levels of creatinine in the blood can lead to protein loss [ 45 , 48 ] . This protein loss could alter the gut environment, potentially favoring the growth of certain microbial species over others and leading to dysbiosis [ 16 , 17 , 45 , 47 ] . Our findings, which demonstrate a significant association between creatinine levels and various gut microbiota taxa, support the hypothesis that renal function has a direct influence on the gut microbiota. Our MR analysis's second step uncovered a link between gut microbiota and cognitive function, with specific taxa exerting both positive and negative impacts on cognitive performance. In humans, the predominant Archaea are methanogens in the gastrointestinal system, which may mitigate ROS and trimethylamine N-oxide (TMAO) production as well as intestinal permeability [ 48 ] . It is established that patients with CKD exhibit dysbiosis and an increased production of uremic toxins, including TMAO, which contribute to oxidative stress and inflammation [ 49 ] . Methane, produced by these methanogens, could play an indirect role in mechanisms that regulate the antioxidant response [ 48 ] . Despite this, some studies offer divergent results. Relative to healthy individuals, methanogens are disproportionately present in patients with conditions such as inflammatory bowel disease [ 50 ] , periodontal disease [ 51 ] , obesity, cancer [ 52 ] , and diverticulosis [ 52 ] . Furthermore, methanogens have been recognized for their capacity to activate dendritic cells from human monocytes, which can lead to potent inflammatory responses [ 53 ] . In recent years, a connection between methanogens and neurological disorders, notably multiple sclerosis (MS), has been noted [ 48 , 54 ] . Our research has revealed an intriguing finding: the class Methanobacteria, the order Methanobacteriales, and the family Methanobacteriaceae may have a negative impact on cognitive ability. However, research into the role of methanogens in inflammation and chronic diseases remains in its early stages, with the precise mechanisms of their impact on human health yet to be fully understood. The Eubacterium fissicatena group is renowned for its species that metabolize butyrate, a short-chain fatty acid (SCFA) derived from dietary carbohydrates [ 55 ] . Butyrate plays a pivotal role in the colonic inflammatory response and possesses anti-inflammatory characteristics. It is also crucial for maintaining the integrity of tight junctions, thereby preventing dysbiosis and gut permeability issues [ 56 , 57 ] . A protective association has been genetically predicted between the abundance of the Eubacterium fissicatena group and Alzheimer's disease (AD) [ 58 ] . Consistent with previous findings, we found that the Eubacterium fissicatena group's negative correlation with the number of fluid intelligence questions attempted within the time limit suggests its potential to detract from cognitive function. These findings support the emerging perspective that the gut microbiota is a modifiable factor that could significantly influence cognitive health. The identification of specific classes and genera that exhibit significant mediation effects provides preliminary evidence for a modifiable pathway that could be targeted in interventions aimed at improving cognitive outcomes in individuals with CKD. Our study's strengths lie in its innovative use of a two-step MR approach and the comprehensive evaluation of a wide range of renal and cognitive biomarkers. The study leverages MR analysis, offering distinct advantages in the investigation of causality. As a novel genetic statistical approach, MR provides a robust framework for discerning causal relationships by utilizing genetic variants as IVs. This method circumvents many of the challenges associated with traditional observational studies, such as confounding factors and the issue of reverse causality. However, our study has several limitations. Firstly, the generalizability of our findings is limited by the fact that the genetic data from GWAS were derived exclusively from individuals of European ancestry, which restricts the applicability of our conclusions to other racial and ethnic groups. Secondly, the potential for significant overlap in SNP data across various taxonomic ranks, such as phylum, class, order, family, and genus, may affect the reproducibility of the MR analysis results. There is the potential for residual confounding due to the exclusion of certain taxa and the reliance on urinary creatinine as a proxy for renal function. This overlap could introduce bias or reduce the precision of the estimated causal effects. Lastly, the limitations of GWAS meta-analysis data prevent the investigation of stratification effects and nonlinear associations between CKD and gut microbiota risk. The assumption of linearity may not capture the complexity of biological processes. Future research incorporating more granular GM classification and diverse ancestry data is warranted to address these limitations and further validate the observed associations. Despite these possible limitations, we have demonstrated through a variety of sensitivity studies that the causal estimates in this study were robust. 5. Conclusion In conclusion, our study applied a two-step MR approach to establish a causal link between CKD and cognitive dysfunction, with the gut microbiome identified as a significant mediator. The study highlighted the complex role of renal function markers like creatinine and cystatin C, with creatinine showing an unexpected positive association with cognitive performance. The research also revealed that specific gut microbes, including the Eubacterium fissicatena group, could either mitigate or exacerbate cognitive decline, suggesting the gut microbiota's modifiability as a target for cognitive health interventions. Methanogens, more prevalent in various diseases, were noted for their dual inflammatory and anti-inflammatory effects, with their precise impact on health needing further exploration. These findings contribute to the growing body of knowledge on the kidney-gut-brain axis and highlight the potential for modulating the gut microbiota to influence cognitive outcomes in individuals with CKD. Declarations Ethics approval and consent to participate: No additional ethical approval is required as this is a re-analysis of data that is already publicly available. Consent for publication: Not applicable. Availability of data and material: All data generated or analysed during this study are included in the article/Additional file. Further inquiries can be directed to the corresponding authors. Competing interests: The authors declare that they have no competing interests. Funding: This research was supported by the National Natural Science Foundation of China (81870850). Authors' contributions: Conceived and designed the experiments: LZ and QR. Performed and analyzed the experiments: LZ, ZW, and MW. Collecting data: LZ and XL. Wrote the manuscript: LZ and ZW. Read and approved the final manuscript: LZ, ZW, MW, XL and QR. Acknowledgements: We wish to acknowledge the participants and investigators of the GWAS used in current study. References Ammirati A L. Chronic Kidney Disease[J]. Revista da Associação Médica Brasileira, 2020,66(suppl 1):s3-s9.DOI: 10.1590/1806-9282.66.s1.3 . de Boer I H, Caramori M L, Chan J C N, et al. KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease[J]. Kidney International, 2020,98(4):S1-S115.DOI: 10.1016/j.kint.2020.06.019 . Vos T, Abajobir A A, Abate K H, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016[J]. 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Borges N A, Barros A F, Nakao L S, et al. Protein-Bound Uremic Toxins from Gut Microbiota and Inflammatory Markers in Chronic Kidney Disease[J]. Journal of Renal Nutrition, 2016,26(6):396–400.DOI: 10.1053/j.jrn.2016.07.005 . Blais Lecours P, Marsolais D, Cormier Y, et al. Increased Prevalence of Methanosphaera stadtmanae in Inflammatory Bowel Diseases[J]. PLoS ONE, 2014,9(2):e87734.DOI: 10.1371/journal.pone.0087734 . Lepp P W, Brinig M M, Ouverney C C, et al. MethanogenicArchaea and human periodontal disease[J]. Proceedings of the National Academy of Sciences, 2004,101(16):6176–6181.DOI: 10.1073/pnas.0308766101 . Guindo C O, Drancourt M, Grine G. Digestive tract methanodrome: Physiological roles of human microbiota-associated methanogens[J]. Microbial Pathogenesis, 2020,149: 104425.DOI:10.1016/j.micpath.2020.104425 . Vierbuchen T, Bang C, Rosigkeit H, et al. The Human-Associated Archaeon Methanosphaera stadtmanae Is Recognized through Its RNA and Induces TLR8-Dependent NLRP3 Inflammasome Activation[J]. Frontiers in Immunology, 2017,8.DOI: 10.3389/fimmu.2017.01535 . Mirza A I, Zhu F, Knox N, et al. Metagenomic Analysis of the Pediatric-Onset Multiple Sclerosis Gut Microbiome[J]. Neurology, 2022,98(10).DOI: 10.1212/WNL.0000000000013245 . Amir I, Bouvet P, Legeay C, et al. Eisenbergiella tayi gen. nov., sp. nov., isolated from human blood[J]. International Journal of Systematic and Evolutionary Microbiology, 2014,64(Pt_3):907–914.DOI: 10.1099/ijs.0.057331-0 . Silva Y P, Bernardi A, Frozza R L. The Role of Short-Chain Fatty Acids From Gut Microbiota in Gut-Brain Communication[J]. Frontiers in Endocrinology, 2020,11.DOI: 10.3389/fendo.2020.00025 . Kelly C J, Zheng L, Campbell E L, et al. Crosstalk between Microbiota-Derived Short-Chain Fatty Acids and Intestinal Epithelial HIF Augments Tissue Barrier Function[J]. Cell Host & Microbe, 2015,17(5):662–671.DOI: 10.1016/j.chom.2015.03.005 . Cammann D, Lu Y, Cummings M J, et al. Genetic correlations between Alzheimer’s disease and gut microbiome genera[J]. Scientific Reports, 2023,13(1).DOI: 10.1038/s41598-023-31730-5 . Additional Declarations No competing interests reported. Supplementary Files Additionalfile.docx Fig.S1.png Fig.S2.png Fig.S3.png TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx 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-4668717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326252930,"identity":"5c6d696a-50aa-4750-b17e-5bc4f04825ab","order_by":0,"name":"Lv Zhou","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Lv","middleName":"","lastName":"Zhou","suffix":""},{"id":326252933,"identity":"c5a10c5a-541f-422c-a637-c00755750fae","order_by":1,"name":"Zhitian Wang","email":"","orcid":"","institution":"Zhongda Hospital Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Zhitian","middleName":"","lastName":"Wang","suffix":""},{"id":326252935,"identity":"6ae0db47-5379-4442-8584-361193829539","order_by":2,"name":"Mengxue Wang","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Mengxue","middleName":"","lastName":"Wang","suffix":""},{"id":326252937,"identity":"513f6b5e-fbca-4a48-9c33-4b28a5f4434e","order_by":3,"name":"Xiao-li Li","email":"","orcid":"","institution":"Zhongda Hospital Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-li","middleName":"","lastName":"Li","suffix":""},{"id":326252938,"identity":"2c2cf862-1d94-4fa8-a8cd-37849e8e1ca1","order_by":4,"name":"Qingguo Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLCCBDDJ2Pj4T4UNDz9/A9FamJsNeM6kyUjOOEC0XextErwth20MGhLwqzNv7z144+GOWrsNxxvbJCQbzvMYMBxg/PAxB7cWmTPnki0SzxxP3nDmYLOF4Y7bPObMDcySM7fh1iIhkWMmkdh2LNngRmLjjcQzt3ksGw6wMfPi0yL/Bq6lQeJg2zkegwMJBLRI8IC01NgBtTRJNrYdIEILT46xRWLbgQRJoF+MGc4k80jOONiM3y/sZwxv/myrs+c73v7wMUOFnT0/f/PBDx/xaAFrY2A4nNiA4DM24FKJrKXOnqCqUTAKRsEoGLkAACdwWJSupOFqAAAAAElFTkSuQmCC","orcid":"","institution":"Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Qingguo","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-07-01 14:22:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4668717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4668717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61015157,"identity":"62e7bdc7-a513-4662-9e55-141349b79997","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":537280,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study. Mendelian randomization study rationale: assumption 1, genetic instruments are associated with exposure; assumption 2, genetic instruments are not associated with confounders; assumption 3, genetic instruments are not associated with outcome, and genetic instruments act on outcome only through exposure. Abbreviations: CKD, chronic kidney disease; N, sample size.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/bb70388b530b7aee0a730698.png"},{"id":61015151,"identity":"4e0f85ef-6334-47c2-ac03-12e22c32865c","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":773369,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for causal effects of exposure on outcome (inverse-variance-weighted method). Abbreviations: OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/f83bc2e21fe04fce4e7c948a.png"},{"id":61016005,"identity":"b35132ed-5562-4ea2-b578-8112832c7b9b","added_by":"auto","created_at":"2024-07-24 15:21:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2696349,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-step MR analyses on the mediation role of gut microbiota. A. Summary MR estimates for the effects of creatinine exposure on identified gut microbiotas in Step1, using the IVW method. B. Summary MR estimates for the effects of identified gut microbiotas on cognitive outcome in Step2, using the IVW method. Abbreviations: OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/0345059e310f8cd8f2d7a5eb.png"},{"id":61015164,"identity":"49529981-933a-4679-a41b-e30d3cda04f2","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":297883,"visible":true,"origin":"","legend":"\u003cp\u003eThe mediation effects of gut microbiota in the associations of exposure with outcome. A. The moderation effects of various levels of gut microbiota on the relationship between creatinine in urine and cognitive performance. B. The moderation effect of genus level of gut microbiota on the relationship between creatinine and Number of fluid intelligence questions attempted within time limit.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/c150ec84106bcb9325b55a33.png"},{"id":62840893,"identity":"7d34beed-0537-44b9-b6bf-4bafaab3e23c","added_by":"auto","created_at":"2024-08-20 06:32:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4490911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/4195c711-e02f-4a00-8503-8dba50fc416d.pdf"},{"id":61016006,"identity":"86ae8a50-6f5d-4657-8a3c-f80456782c5d","added_by":"auto","created_at":"2024-07-24 15:21:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":129791,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/09f8cc26eb64142564917596.docx"},{"id":61017049,"identity":"ebacca1f-7c1f-48cf-a014-06317f46243e","added_by":"auto","created_at":"2024-07-24 15:29:11","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":650540,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/2f5234280078b4a4faca39a8.png"},{"id":61016004,"identity":"130ed497-fedc-49b2-86ae-786e54b3cd1c","added_by":"auto","created_at":"2024-07-24 15:21:11","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1126693,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/816e7d0728aea05887f692e2.png"},{"id":61017048,"identity":"8084d3ad-4c59-447e-91c0-360335375cc2","added_by":"auto","created_at":"2024-07-24 15:29:11","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":412988,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.png","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/99ca692032c1e2d9562d08ba.png"},{"id":61015154,"identity":"45f6d438-46ec-4974-a2d4-4c36050ec1a5","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15673,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/3ccb3e00f948f8cf3ee8b053.xlsx"},{"id":61017051,"identity":"21c82f0a-279a-4a39-9052-ef6b5c296c9a","added_by":"auto","created_at":"2024-07-24 15:29:11","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":76019,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/218bc172ca074ca872f10820.xlsx"},{"id":61015162,"identity":"04b4c27e-e545-42a7-9ad0-70e5bfa454f5","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15278,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/f2f8dbf05e3a0618bf0f2572.xlsx"},{"id":61015160,"identity":"17448348-94f0-4995-b0ff-85bfd29958e0","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":364522,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/d5ce2d4de9d6a4ff6ab94bcb.xlsx"},{"id":61015159,"identity":"ab56679e-ffd0-4972-90ae-3143cfc8620a","added_by":"auto","created_at":"2024-07-24 15:13:11","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":439764,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/b7526b859528b30a7f2eafe1.xlsx"},{"id":61016008,"identity":"2e2ed496-a3c6-459b-9841-09635a36a66c","added_by":"auto","created_at":"2024-07-24 15:21:11","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1392149,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4668717/v1/b862c58082ad21b50665c164.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut microbiota mediating the effect of chronic kidney disease on cognitive dysfunction: a mendelian randomization study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eKidney function continuously declines as a chronic condition known as chronic kidney disease (CKD)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. One of the major worldwide health issues now being faced by the aging global population is the rising prevalence of CKD, which has been on the rise annually\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. According to estimates, 10% of people worldwide suffer from CKD\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and the condition is linked to a significant illness burden and mortality risk\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCognitive dysfunction is characterized as diminished or impaired mental and/or intellectual performance, which includes deficits in memory, executive functioning, attention, language, and visuospatial function\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. This impairment can have a significant impact on an individual's daily routine, work, and social activities, lowering their quality of life.\u003c/p\u003e \u003cp\u003eIn recent years, an increasing number of studies have focused on the relationship between CKD and cognitive dysfunction. According to data from the United States and Denmark, the prevalence of cognitive dysfunction among dialysis patients is between 16 and 38%\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, which is more than three to five times that of general groups with normal renal function\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Furthermore, patients with CKD and their families report that their memory deteriorates, as do their language abilities, numeracy, understanding, and judgement, all of which have a substantial impact on their quality of life in severe instances\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Some investigations have demonstrated that cognitive dysfunction has a strong positive link with CKD and prognostic markers such as long-term all-cause mortality in dialysis patients\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDomestic and international studies on the relationship between cognitive dysfunction and CKD are currently in the exploratory stage, and there is still debate about the causal relationship between CKD and cognitive dysfunction. Some studies indicate that CKD may cause cognitive dysfunction\u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, while others imply that cognitive dysfunction may be caused by other factors such as age, education level, vascular disease\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, hypertension\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and diabetes mellitus\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, previous studies have revealed limitations, including insufficient statistical robustness and some shortcomings in making causal inferences due to small population sizes, different interpretations of the findings, and the presence of confounders such as socio-cultural demographic characteristics and pre-existing cerebrovascular events.\u003c/p\u003e \u003cp\u003eIn addition, the particular mechanisms underlying cognitive dysfunction caused by CKD have yet to be fully understood, requiring further research. Declining kidney function results in increased blood creatinine and urea, causing protein loss and heightened reactive oxygen species (ROS), which also modifies the gut microbiota composition\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Previous research indicates that gut microbiota alterations can affect cognitive function via immune system modulation, neuroactive metabolite production, and gut barrier interaction\u003csup\u003e[\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Given the established connection between the gut microbiota and the brain, it is plausible that the gut microbiota may also be a critical link between renal function and cognitive health.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a method that uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for causal inference\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Large-scale genome-wide association studies (GWAS) have been used to assess risk factors for cognitive dysfunction, exploring causal associations between exposure factors like hypertension, inflammatory bowel disease, and coronary heart disease. Additionally, GWAS studies have identified SNP loci significantly associated with CKD\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, providing a basis for MR studies to investigate causal associations between CKD and various diseases, including cognitive dysfunction.\u003c/p\u003e \u003cp\u003eThis study aimed to explore the potential genetic causality between CKD and cognitive dysfunction using two-sample Mendelian randomization. Furthermore, mediated Mendelian randomization was used to investigate potential genetic mechanisms. This approach could provide new insights into the mechanisms linking renal health with cognitive function alterations, possibly through gut microbiota modulation. It offers a fresh viewpoint on the treatment and prognosis of chronic kidney disease, as well as a new target for the prevention and therapy of cognitive impairment.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study had a two-part design, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To explore the causal link between CKD and cognitive dysfunction, we used a two-sample MR. Then, we employed a two-step MR methodology (also known as mediated Mendelian randomization) to identify the gut microbiota's mediating role in the association between cognitive dysfunction and CKD.\u003c/p\u003e \u003cp\u003eThe study analyzed publicly accessible summary information on CKD, cognitive dysfunction, and gut microbiota (GM) from prior research or consortiums. There was no need to reapply to the institutional review board (IRB) for permission because each of these studies had already received clearance from its IRB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Source\u003c/h2\u003e \u003cp\u003eThe characteristics of the corresponding GWAS data sources are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study used seven indexes to predict CKD exposure, including CKD, GFR, creatinine, creatinine in urine, urinary albumin excretion, and cystatin C. The statistics were sourced from the IEU open GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as GWAS-IDs of \"ebi-a-GCST003374\" for CKD, \"ebi-a-GCST003372\" for GFR, \"met-d-Creatinine\" for creatinine, \"ukb-a-333\" for creatinine in urine, \"ebi-a-GCST006586\" for urinary albumin excretion, and \"ukb-d-30720_irnt\" for cystatin C. The sample size included 12,385 cases and 104,780 controls for CKD, 133,413 European participants for GFR, 110,058 for creatinine, 327,525 for urine creatinine, 382,500 for urinary albumin excretion, and 24,810 for cystatin C. The study examined genome-wide associations of cognitive outcome levels among European participants, focusing on eight indexes. The statistics were sourced from the IEU open GWAS database, examining cognitive performance (ebi-a-GCST006572), cognitive functions (ieu-b-4838), mean time to correctly identify matches (ukb-b-16287), number of fluid intelligence questions attempted within time limit (ukb-b-2988), number of symbol digit matches made correctly (ukb-b-15625), prospective memory result (ukb-b-4282), round of numeric memory test (ukb-b-16872), and time to answer (ukb-b-14360). Detailed information is provided in the Additional File, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe gut microbiota data was sourced from the latest GWAS summary data, analyzed by the MiBioGen consortium, which included 211 gut microbiota taxa from 18,340 individuals\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The study included 78% of Europeans and used only taxa present in more than 10% of samples to identify genetic loci affecting relative abundance, resulting in 211 taxa, including 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Instrumental Variables Selection\u003c/h2\u003e \u003cp\u003eThe cornerstone of MR studies lies in the prudent selection of IVs. A judicious choice of IVs is fundamental to securing unbiased estimates of causal relationships. SNPs were screened based on the following criteria: (1) A P value\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e indicated a significant connection between SNPs and exposure. For GWAS studies with small sample numbers, the criterion was reduced to P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. (2) Subsequently, using the 1000 Genomes reference panel, independent SNPs were clumped at a stringent linkage disequilibrium (LD) threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In cases where no shared SNPs existed between the outcome and exposure, proxies derived from the European reference panel 1000 Genomes (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.8) were incorporated. (3) SNPs with an effect allele frequency exceeding 0.01 were included, while those with an F-statistic below 10 were excluded, as a measure to ensure the robustness of the instrumental variables and to mitigate the risk of weak instrumental bias\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. (4) PhenoScanner V2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to scrutinize the collected SNPs for potential confounders and bypassing factors, including but not limited to age, gender, race, and other illnesses\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MR analysis\u003c/h2\u003e \u003cp\u003eAll MR analyses were performed in R (version 4.3.1) software, using the \u0026ldquo;TwoSampleMR\u0026rdquo; (version 0.5.7) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MRCIEU/TwoSampleMR\u003c/span\u003e\u003cspan address=\"https://github.com/MRCIEU/TwoSampleMR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e and \u0026ldquo;MR-PRESSO\u0026rdquo; (version 1.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rondolab/MR-PRESSO\u003c/span\u003e\u003cspan address=\"https://github.com/rondolab/MR-PRESSO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e packages.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Primary analysis\u003c/h2\u003e \u003cp\u003eThe inverse-variance-weighted (IVW) method, a conventional approach in Mendelian randomization, was applied for estimating effects. Outcomes were presented as odds ratios (OR) with 95% confidence intervals (CI). Significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical significance was set at a nominal threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eA random-effects model was employed to account for heterogeneity, which was quantified using Cochran's Q test\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The IVW method, despite providing the most accurate effect estimates, overlooked null instruments and pleiotropy. To address these limitations, four alternative approaches were utilized: the weighted median estimation model (WME), the weighted mode approach (WM), the MR-Egger regression model, and the simple mode (SM), each assuming different levels of horizontal pleiotropy\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. To ensure a robust causal inference from the MR estimate and sensitivity analysis methods previously outlined, the following criteria were deemed essential: (1) Consistent directional findings across the five methods were required for the MR estimate and sensitivity analysis. (2) The MR-Egger intercept test must indicate the absence of significant horizontal pleiotropy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Mediation analysis\u003c/h2\u003e \u003cp\u003eThe exploration of mediating effects generates three parameters: the direct effect of exposure on the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, XY), the effect of exposure on the mediating variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, XZ), and the effect of the mediating variable on the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, ZY). If all three parameters are significant, it indicates a causal association from exposure to outcome, partially mediated by the mediator variable. If XY is significant but at least one of XZ and ZY is not significant, no mediation occurs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genetic causality and correlation between CKD and cognitive dysfunction\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Selection of IVs related to CKD\u003c/h2\u003e \u003cp\u003eSix carefully selected sets of IVs were meticulously tailored to accurately reflect the spectrum of CKD, encompassing essential biomarkers such as GFR, creatinine, creatinine in urine, urinary albumin excretion, and cystatin C. We conducted a comprehensive evaluation of the F-statistic values for each IV and for every set of IVs, confirming their robustness as strong IVs with F-statistics exceeding the threshold of 10. Following a series of quality control protocols, the lists of included SNPs were presented in the Additional File, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Two-sample Mendelian randomization analysis\u003c/h2\u003e \u003cp\u003eAfter ascertaining that all IVs were valid under existing conditions, we performed a two-sample MR analysis for the relationship between CKD and cognitive dysfunction. In our application of the IVW method, several significant associations were observed between several renal and cognitive biomarkers, which are detailed as follows: Creatinine was associated with a decreased likelihood of attempting more fluid intelligence questions within the time limit, with an odds ratio (OR) of 0.911 (95% CI [0.839, 0.989], P\u0026thinsp;=\u0026thinsp;0.027). The overall cognitive performance showed a similar trend with creatinine intake, yielding an OR of 0.962 (95% CI [0.931, 0.993], P\u0026thinsp;=\u0026thinsp;0.018). Interestingly, urinary creatinine levels were positively associated with cognitive performance, with an OR of 1.194 (95% CI [1.025, 1.391], P\u0026thinsp;=\u0026thinsp;0.023). Urinary albumin excretion demonstrated an inverse relationship with cognitive performance for both the number of symbol-digit matches made correctly (OR\u0026thinsp;=\u0026thinsp;0.861, 95% CI [0.754, 0.984], P\u0026thinsp;=\u0026thinsp;0.028) and the round of the numeric memory test (OR\u0026thinsp;=\u0026thinsp;0.860, 95% CI [0.742, 0.997], P\u0026thinsp;=\u0026thinsp;0.046). Serum cystatin C levels were positively correlated with the number of symbol-digit matches made correctly, with an OR of 1.029 (95% CI [1.002, 1.056], P\u0026thinsp;=\u0026thinsp;0.036). GFR also showed a positive association with the number of symbol-digit matches made correctly, as indicated by an OR of 1.234 (95% CI [1.001, 1.554], P\u0026thinsp;=\u0026thinsp;0.049). These findings are also illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and elaborated upon in the Additional File, including Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEmploying a variety of statistical methodologies, we observed consistent directional estimates between renal and cognitive biomarkers, thereby reinforcing the validity and robustness of our principal findings (Additional File, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). However, the relationship between GFR and the number of symbol-digit matches made correctly presented a notable exception, with discordant directional estimates. To ensure the fidelity and reliability of the reported causal relationships and to be in line with best practices in Mendelian randomization studies, GFR was excluded from the primary outcome presentation in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eScatter plots and forest plots consistently depicted the causal effects of chronic kidney disease (CKD) on cognitive dysfunction (Additional File, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). No significant horizontal pleiotropy was detected. Furthermore, no significant heterogeneity was observed. The detailed data are presented in the Additional File, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e. The leave-one-out analysis confirmed the stability of the causal effect, indicating that the impact of CKD on cognitive dysfunction is not significantly influenced by any single nucleotide polymorphism (SNP) (Additional File, Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The gut microbiota mediates the causal role of CKD on cognitive dysfunction\u003c/h2\u003e \u003cp\u003eIn our MR study, we found a protective effect of creatinine on cognitive performance, with an odds ratio of 0.911 for fluid intelligence questions and 0.962 for overall cognitive performance. However, creatinine in urine had an OR of 1.194 for cognitive performance, suggesting a divergent mechanism. Previous research has indicated a relationship between creatinine levels and alterations in the gut microbiome. Building on these findings and given the established role of creatinine as an indicator of renal function, our forthcoming research will pivot to an in-depth investigation of creatinine. We conducted a mediation MR study to determine if the GM mediates the causal effects of CKD on cognitive dysfunction.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Selection of IVs related to GM\u003c/h2\u003e \u003cp\u003eFrom a total of 211 gut microbiota taxa, the study eventually identified 122,110 variation sites at five levels: phylum, class, order, family, and genus. To ensure the accuracy of the data, our study excluded 15 bacterial taxa from unknown families or genera, leaving 196. The median F-statistic was 21.0 (ranging from 14.6 to 88.4) for GM, and an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 is considered sufficiently informative for MR analyses (Additional File, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Mediation analysis\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed two-sample analyses to investigate the relationship between creatinine levels in both blood and urine and the gut microbiota (step 1 MR). The results of the IVW analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, whereby the relationship between urine creatinine levels and seven distinct gut microbiota taxa is explained. Six of these taxa showed consistent causal effects. Notably, the class Methanobacteria, the order Methanobacteriales, the family Methanobacteriaceae, and three genera\u0026mdash;Eubacterium rectale group, Howardella, and Rikenellaceae RC9 gut group\u0026mdash;were found to be negatively correlated with creatinine levels. On the other hand, the genus Ruminococcaceae (UCG-004) showed a positive connection. Additionally, creatinine levels demonstrated significant associations with six other gut microbiota taxa, four of which indicated consistent causality. The genus Barnesiella and the family Peptococcaceae showed positive relationships. On the other hand, there were negative connections found with four genera: Terrisporobacter, Lachnoclostridium, Bilophila, and Eubacterium rectale group.\u003c/p\u003e \u003cp\u003eSimilarly, we conducted two-sample analyses to examine the relationship between gut microbiota and cognitive dysfunction (step 2 MR). We identified 10 gut microbiota taxa that exhibited causal effects on cognitive dysfunction. Specifically, three taxa were associated with an increased risk of impaired cognitive performance: the class Methanobacteria, the order Methanobacteriales, and the family Methanobacteriaceae. In contrast, two genera, Barnesiella and RuminococcaceaeUCG-004, were found to potentially decrease the risk of adverse prospective memory outcomes, while the genus Bilophila was associated with an increased risk for this cognitive domain. The IVW analysis depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB revealed significant positive correlations between the genus Eubacterium brachygroup and Lachnoclostridium with the number of symbol-digit matches made correctly. However, the Eubacterium brachygroup showed a significant negative correlation with the round of numeric memory test performance. Additionally, the genus Howardella was positively correlated with time to answer, while Bilophila was associated with the mean time to correctly identify matches. The Rikenellaceae RC9 gut group demonstrated a positive correlation with the number of fluid intelligence questions attempted within the time limit, while the Eubacterium fissicatena group demonstrated a negative correlation.\u003c/p\u003e \u003cp\u003eTo further validate the robustness of our findings, we performed a series of sensitivity analyses, detailed in Additional File, Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e. The majority of these analyses yielded consistent results, albeit with broader confidence intervals. Cochran\u0026rsquo;s Q test results, all exceeding the threshold of 0.05, indicated no significant heterogeneity among the studies (Additional File, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The MR-PRESSO analysis confirmed the absence of influential outliers among SNPs. Furthermore, both the MR-Egger intercept test and the global test p-values showed no evidence of horizontal pleiotropy, reinforcing the credibility of our MR findings.\u003c/p\u003e \u003cp\u003eUtilizing a two-step Mendelian randomization (MR) approach with the 'product of coefficients' method, we examined whether the gut microbiota mediates the relationship between urinary creatinine levels and cognitive performance. The mediation effect was determined by the ratio of the gut microbiota-mediated effect to the total effect of urinary creatinine on cognition. Our study revealed that the class Methanobacteria, order Methanobacteriales, and family Methanobacteriaceae partially moderate this association, with a mediation proportion of 7.97%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The critical role that the Eubacterium rectale group plays as a mediator in the causal pathway that links creatinine levels to the number of fluid intelligence questions attempted within the time limit, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, is highlighted by the mediation analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study used a two-sample MR method to explore the causal relationship between CKD and cognitive dysfunction. The MR results showed that this causal connection existed. Furthermore, we also discovered a link between CKD and cognitive dysfunction via the gut microbiome using a two-step MR study. Encouragingly, we found that some gut microbiota could mediate or moderate this relationship.\u003c/p\u003e \u003cp\u003eThe two-sample MR analysis revealed intriguing associations between renal biomarkers and cognitive performance. In some ways, the current study is similar to earlier research, but not completely. Previous studies have mixed results, with most comparing cognitive outcomes based on eGFR\u003csup\u003e[\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Due to discrepancies in the directional estimations of the link between GFR and cognitive function, GFR was excluded from our primary outcome presentation. Studies involving kidney damage markers (i.e., albuminuria or proteinuria) have been more consistent in reporting cognitive function associations\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. This is in line with our observations that urinary albumin excretion demonstrated an inverse relationship with cognitive performance, indicating that albuminuria could be a potential risk factor for cognitive impairment. Furthermore, our study contributes to the emerging evidence that serum cystatin C may be a risk factor for cognitive decline. This finding is consistent with the hypothesis that inflammation and vascular damage, which are often associated with kidney disease, can have detrimental effects on cognitive function\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Conversely, the observation that high creatinine levels may improve memory and learning functions challenges conventional understanding\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Our study suggests a potential protective role of creatinine in cognitive processes, as indicated by the ORs for fluid intelligence questions and overall cognitive performance. The unexpected positive association between urinary creatinine levels and cognitive performance introduces a layer of complexity. This warrants further investigation, as it could indicate a more complex interplay between renal function markers and cognitive health than previously recognized. This divergence may reflect distinct metabolic pathways or the influence of other, as yet undefined, factors.\u003c/p\u003e \u003cp\u003eThe exploration of the GM mediating role in the causal pathway from CKD to cognitive dysfunction represents a significant advancement in our understanding of the complex interplay between renal health and cognitive function. Building upon previous research that implicated creatinine levels and alterations in the GM, our study introduces a two-step MR approach. This methodological choice allowed us to rigorously investigate the potential mediating effects of the GM in a manner that leverages the strengths of genetic instrumental variables to infer causality.\u003c/p\u003e \u003cp\u003eCreatinine, a metabolic waste product, accumulates in the blood as kidney function declines\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Healthy kidneys easily remove creatinine from the circulation via urine; however, impaired kidney function can result in lower creatinine excretion in the urine\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The elevated levels of creatinine in the blood can lead to protein loss\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. This protein loss could alter the gut environment, potentially favoring the growth of certain microbial species over others and leading to dysbiosis\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Our findings, which demonstrate a significant association between creatinine levels and various gut microbiota taxa, support the hypothesis that renal function has a direct influence on the gut microbiota.\u003c/p\u003e \u003cp\u003eOur MR analysis's second step uncovered a link between gut microbiota and cognitive function, with specific taxa exerting both positive and negative impacts on cognitive performance. In humans, the predominant Archaea are methanogens in the gastrointestinal system, which may mitigate ROS and trimethylamine N-oxide (TMAO) production as well as intestinal permeability\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. It is established that patients with CKD exhibit dysbiosis and an increased production of uremic toxins, including TMAO, which contribute to oxidative stress and inflammation\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Methane, produced by these methanogens, could play an indirect role in mechanisms that regulate the antioxidant response\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Despite this, some studies offer divergent results. Relative to healthy individuals, methanogens are disproportionately present in patients with conditions such as inflammatory bowel disease\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, periodontal disease\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e, obesity, cancer\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e, and diverticulosis\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Furthermore, methanogens have been recognized for their capacity to activate dendritic cells from human monocytes, which can lead to potent inflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. In recent years, a connection between methanogens and neurological disorders, notably multiple sclerosis (MS), has been noted\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Our research has revealed an intriguing finding: the class Methanobacteria, the order Methanobacteriales, and the family Methanobacteriaceae may have a negative impact on cognitive ability. However, research into the role of methanogens in inflammation and chronic diseases remains in its early stages, with the precise mechanisms of their impact on human health yet to be fully understood.\u003c/p\u003e \u003cp\u003eThe Eubacterium fissicatena group is renowned for its species that metabolize butyrate, a short-chain fatty acid (SCFA) derived from dietary carbohydrates\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Butyrate plays a pivotal role in the colonic inflammatory response and possesses anti-inflammatory characteristics. It is also crucial for maintaining the integrity of tight junctions, thereby preventing dysbiosis and gut permeability issues\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. A protective association has been genetically predicted between the abundance of the Eubacterium fissicatena group and Alzheimer's disease (AD)\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Consistent with previous findings, we found that the Eubacterium fissicatena group's negative correlation with the number of fluid intelligence questions attempted within the time limit suggests its potential to detract from cognitive function. These findings support the emerging perspective that the gut microbiota is a modifiable factor that could significantly influence cognitive health. The identification of specific classes and genera that exhibit significant mediation effects provides preliminary evidence for a modifiable pathway that could be targeted in interventions aimed at improving cognitive outcomes in individuals with CKD.\u003c/p\u003e \u003cp\u003eOur study's strengths lie in its innovative use of a two-step MR approach and the comprehensive evaluation of a wide range of renal and cognitive biomarkers. The study leverages MR analysis, offering distinct advantages in the investigation of causality. As a novel genetic statistical approach, MR provides a robust framework for discerning causal relationships by utilizing genetic variants as IVs. This method circumvents many of the challenges associated with traditional observational studies, such as confounding factors and the issue of reverse causality.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations. Firstly, the generalizability of our findings is limited by the fact that the genetic data from GWAS were derived exclusively from individuals of European ancestry, which restricts the applicability of our conclusions to other racial and ethnic groups. Secondly, the potential for significant overlap in SNP data across various taxonomic ranks, such as phylum, class, order, family, and genus, may affect the reproducibility of the MR analysis results. There is the potential for residual confounding due to the exclusion of certain taxa and the reliance on urinary creatinine as a proxy for renal function. This overlap could introduce bias or reduce the precision of the estimated causal effects. Lastly, the limitations of GWAS meta-analysis data prevent the investigation of stratification effects and nonlinear associations between CKD and gut microbiota risk. The assumption of linearity may not capture the complexity of biological processes. Future research incorporating more granular GM classification and diverse ancestry data is warranted to address these limitations and further validate the observed associations. Despite these possible limitations, we have demonstrated through a variety of sensitivity studies that the causal estimates in this study were robust.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study applied a two-step MR approach to establish a causal link between CKD and cognitive dysfunction, with the gut microbiome identified as a significant mediator. The study highlighted the complex role of renal function markers like creatinine and cystatin C, with creatinine showing an unexpected positive association with cognitive performance. The research also revealed that specific gut microbes, including the Eubacterium fissicatena group, could either mitigate or exacerbate cognitive decline, suggesting the gut microbiota's modifiability as a target for cognitive health interventions. Methanogens, more prevalent in various diseases, were noted for their dual inflammatory and anti-inflammatory effects, with their precise impact on health needing further exploration. These findings contribute to the growing body of knowledge on the kidney-gut-brain axis and highlight the potential for modulating the gut microbiota to influence cognitive outcomes in individuals with CKD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e No additional ethical approval is required as this is a re-analysis of data that is already publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eAll data generated or analysed during this study are included in the article/Additional file. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by the National Natural Science Foundation of China (81870850).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eConceived and designed the experiments: LZ and QR. Performed and analyzed the experiments: LZ, ZW, and MW. Collecting data: LZ and XL. Wrote the manuscript: LZ and ZW. Read and approved the final manuscript: LZ, ZW, MW, XL and QR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe wish to acknowledge the participants and investigators of the GWAS used in current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmmirati A L. Chronic Kidney Disease[J]. Revista da Associa\u0026ccedil;\u0026atilde;o M\u0026eacute;dica Brasileira, 2020,66(suppl 1):s3-s9.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1806-9282.66.s1.3\u003c/span\u003e\u003cspan address=\"10.1590/1806-9282.66.s1.3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Boer I H, Caramori M L, Chan J C N, et al. KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease[J]. 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Scientific Reports, 2023,13(1).DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-31730-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-31730-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Chronic kidney disease, Gut microbiota, Cognitive dysfunction, mediation Mendelian randomization, creatinine, methanogens","lastPublishedDoi":"10.21203/rs.3.rs-4668717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4668717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between chronic kidney disease (CKD) and cognitive dysfunction is still debated, and the mechanisms underlying cognitive dysfunction caused by CKD are still not fully understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study aimed to explore the potential genetic causality between CKD and cognitive dysfunction using two-sample Mendelian randomization. Furthermore, mediated Mendelian randomization was used to investigate potential genetic mechanisms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study utilizes a two-step Mendelian randomization approach to establish a causal link between chronic kidney disease and cognitive dysfunction, with the gut microbiome playing a pivotal mediating role. The study underscores the intricate relationship between renal function markers\u0026mdash;particularly creatinine, which paradoxically correlates positively with cognitive performance\u0026mdash;and cognitive health, while also pointing to the modifiable nature of specific gut microbes, such as the Eubacterium fissicatena group, as potential influencers of cognitive decline. Additionally, methanogens' presence in diseases and their dual role in inflammation suggest a complex impact on health that warrants deeper investigation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study found a causal link between CKD and cognitive dysfunction, with the gut microbiome acting as a mediator.\u003c/p\u003e","manuscriptTitle":"Gut microbiota mediating the effect of chronic kidney disease on cognitive dysfunction: a mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 15:13:06","doi":"10.21203/rs.3.rs-4668717/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":"d9b73776-9cb3-49ad-8cd9-3181a2931df6","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-20T06:24:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 15:13:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4668717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4668717","identity":"rs-4668717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}