Causal relationship between gut microbiota and chronic renal failure: a two-sample Mendelian randomization study.

Background and Aims Previous epidemiological studies have suggested a potential association between constipation and chronic kidney disease (CKD). The high prevalence of constipation among CKD patients and the longitudinal relationship between constipation and incident CKD support this association. However, the causal relationship between these two conditions remains unclear. To investigate this, we performed a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary statistics from large biobank datasets. Method GWAS summary data on constipation and CKD were obtained from the FinnGen (DF12) and CKDGen consortium, comprising sample sizes of 500,348 and 480,698, respectively. Constipation was defined using ICD-10 codes and laxative medication purchases, while CKD was defined as a creatinine-based estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m². Genetic instruments for CKD and constipation were selected to evaluate the bidirectional relationship between the two conditions. Independent instrumental variables were identified based on significant associations with the exposure (P < 5 × 10⁻⁸, clumping window of 10,000 kb, and r² < 0.001). Due to the limited number of SNPs associated with constipation, a more relaxed threshold (P < 5 × 10⁻⁶) was applied for instrument selection. Results A total of 19 and 21 independent instrumental variables were selected for constipation and CKD, respectively. The MR analysis did not reveal a significant association between genetically predicted constipation and CKD (inverse-variance weighted [IVW] Or = 1.033, 95% CI: 0.898–1.190). However, reverse MR analysis indicated that genetically predicted CKD was causally associated with an increased risk of constipation (IVW Or = 1.052, 95% CI: 1.003–1.104). No evidence of horizontal pleiotropy was observed. Conclusion Our findings suggest that constipation is not causally associated with CKD; rather, CKD may have a causal impact on the development of constipation.

Although prior observational studies have suggested that patients with non-alcohol fatty liver disease (NAFLD) may have a higher risk of coronary artery calcification (CAC), these findings remain controversial. This study aimed to explore the causal association between NAFLD and CAC at genetic level by two-sample Mendelian randomization (MR) analysis. Utilizing summary-level data from multiple large-scale genome-wide association studies (GWAS) in European populations, a two-sample MR analysis was initially conducted to explore the potential causal association between NAFLD and CAC. The results of the MR analysis were pooled through random-effect meta-analysis. The inverse variance weighting (IVW) method served as the primary approach for MR analysis. Additionally, the weighted median, MR-Egger and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods was applied for sensitivity analysis. Summary-level data on liver fatty content was utilized for validation analysis, while summary-level data on cirrhosis served as positive control, further ensuring the validity and robustness of our findings. Reverse MR analysis was performed to assess the association between CAC and NAFLD, employing instrument variables derived from CAC. The MR analysis indicated that genetically predicted NAFLD had no effects on the risk of CAC (Beta: 0.01, 95% CI: -0.02 to 0.03, P = 0.74). Likewise, the reverse MR analysis found no significant genetic association between CAC and NAFLD (OR: 1.00, 95% CI: 0.96 to 1.06, P = 0.88). Validation analysis yielded consistent results, showing no significant association between fatty liver content and CAC. Our two-sample MR analysis did not support that there is a causal association between NAFLD and CAC at genetic level. The association between NAFLD and CAC reported in some previous observational studies may rely on NAFLD complicated with metabolic disorders, rather than being directly linked to the hepatic steatosis.

The interplay between gut microbiome genera and inflammatory kidney-related diseases, such as nephrotic syndrome, glomerulonephritis, tubulo-interstitial nephritis, and chronic kidney disease, has been observed. However, the causal relationships between specific bacterial genera and these renal diseases have not been fully elucidated. To investigate the potential causal links between different genera of the gut microbiome and the susceptibility to various renal conditions utilizing two-sample Mendelian randomization (MR) analyses. Genome-wide association study (GWAS) summary statistics of gut microbiota and inflammatory kidney-related diseases were obtained from published GWASs. Two-sample MR analyses were conducted using methods including inverse-variance weighted (IVW), MR Egger, and others to identify potential causal links between gut microbial genera and renal conditions. Sensitivity analyses, including Cochran's Q test and the MR-PRESSO global test, were performed to validate the robustness of the results and detect horizontal pleiotropy. In addition, a reverse MR analysis was conducted to assess reverse causation possibilities. By synthesizing insights from both primary and sensitivity analyses, this study unveiled critical associations of 12 bacterial genera with nephrotic syndrome, 7 bacterial genera with membranous nephropathy, 3 bacterial genera with glomerulonephritis, 4 bacterial genera with acute tubulo-interstitial nephritis, 6 bacterial genera with chronic tubulo-interstitial nephritis, and 7 bacterial genera with chronic kidney disease. Various genera were pinpointed as having either positive or negative causal relationships with these renal conditions, as evidenced by specific ranges of IVW-OR values (all P< 0.05). The congruence of the sensitivity analyses bolstered the primary findings, displaying no marked heterogeneity or horizontal pleiotropy. Notably, the reverse MR analysis with nephritis as the exposure did not reveal any causal relationships, thereby strengthening the resilience and validity of the primary associations. This study explored the causal associations between several gut microbial genera and the risk of several inflammatory kidney-related diseases, uncovering several associations between specific gut microbial genera and nephrotic syndrome, membranous nephropathy, glomerulonephritis, tubulo-interstitial nephritis, and chronic kidney disease. These findings enhance our understanding of the complex interplay between the gut microbiome and kidney diseases, and they will be beneficial for early diagnosis and subsequent treatment.

The correlation between obesity and primary open-angle glaucoma (POAG) has not yet been fully established. The aim of this study was to investigate the causal relationship between obesity and POAG by a two-sample Mendelian randomization (MR) study. In this study, body mass index (BMI), an index to evaluate general obesity, and waist and hip circumference, indices to evaluate abdominal obesity, were selected as exposures in MR analysis. Single-nucleotide polymorphisms (SNPs) were chosen as instrumental variables (IVs). Summary data from genome-wide association studies (GWASs) based on a European ancestry by Locke et al., with regard to BMI, and Shungin et al., with regard to waist and hip circumference, were used. Genetic predictors of POAG were obtained from public GWAS summary data. To assess the causal effect of obesity on POAG, the inverse variance-weighted (IVW) method was used as the primary method, and other methods, such as MR–Egger, weighted median, simple mode, and weighted mode, were also used as complementary analyses. Finally, we performed Cochran’s Q statistic to assess heterogeneity, and sensitivity analysis was performed to evaluate the reliability and stability of the MR results. MR analysis showed that BMI has a positive effect on the risk of POAG, with 1 standard deviation (SD) increase in BMI; the risk of POAG increases by approximately 90.9% [OR = 1.909; 95% CI= (1.225, 2.975); p = 0.0042)] (analyzed by IVW); there were no heterogeneity and pleiotropy in the result; and waist circumference also had a positive effect on the risk of POAG [OR = 2.319; 95% CI= (1.071, 5.018); p = 0.033)] analyzed by weighted median. As hip circumference increases, with 1 SD increase in hip circumference, the risk of POAG increases by approximately 119% [OR = 2.199; 95% CI= (1.306, 3.703); p = 0.00305)] estimated by IVW, there were not heterogeneity and pleiotropy as for the result. Our study for the first time confirms that obesity might increase the risk of POAG using two-sample MR analysis. These results might provide guidance on the prevention and treatment of POAG.

BackgroundRecent research has established the correlation between gut microbiota and periodontitis via oral-gut axis. Intestinal dysbiosis may play a pivotal bridging role in extra-oral inflammatory comorbidities caused by periodontitis. However, it is unclear whether the link is merely correlative or orchestrated by causative mechanistic interactions. This two-sample Mendelian randomization (MR) study was performed to evaluate the potential bidirectional causal relationships between gut microbiota and periodontitis.Materials and MethodsA two-sample MR analysis was performed using summary statistics from genome-wide association studies (GWAS) for gut microbiota (n = 18,340) and periodontitis (cases = 12,251; controls = 22,845). The inverse-variance weighted (IVW) method was used for the primary analysis, and we employed sensitivity analyses to assess the robustness of the main results. The PhenoScanner database was then searched for pleiotropy SNPs associated with potential confounders. In order to identify the possibly influential SNPs, we further conducted the leave-one-out analysis. Finally, a reverse MR analysis was performed to evaluate the possibility of links between periodontitis and genetically predicted gut microbiota alternation.Results2,699 single nucleotide polymorphisms (SNPs) associated with 196 microbiota genera were selected as instrumental variables (IVs). IVW method suggested that order Enterobacteriales (OR: 1.35, 95% CI 1.10–1.66), family Bacteroidales S24.7group (OR: 1.22, 95% CI 1.05–1.41), genus Lachnospiraceae UCG008 (OR: 1.16, 95% CI 1.03–1.31), genus Prevotella 7 (OR: 1.11, 95% CI 1.01–1.23), and order Pasteurellales (OR: 1.12, 95% CI 1.00–1.26) may be associated with a higher risk of periodontitis, while genus Ruminiclostridium 6 may be linked to a lower risk (OR: 0.82, 95% CI 0.70–0.95). The sensitivity and heterogeneity analyses yielded no indication of horizontal pleiotropy or heterogeneity. Only the association between order Enterobacteriales and the likelihood of periodontitis remained consistent across all alternative MR approaches. In the reverse MR analysis, four microbiota genera were genetically predicted to be down-regulated in periodontitis, whereas two were predicted to be up-regulated.ConclusionsThe present MR analysis demonstrated the potential bidirectional causal relationships between gut microbiota and periodontitis. Our research provided fresh insights for the prevention and management of periodontitis. Future research is required to support the finding of our current study.

The gut microbiome is closely associated with malignant tumors; however the specific mechanisms by which it contributes to the development of lung adenocarcinoma remain unclear. In this study, we performed a two-sample bidirectional Mendelian randomization (MR) analysis to assess the causal relationship between the gut microbiome and lung adenocarcinoma. By identifying single nucleotide polymorphism markers linked to gut microbiome species, we aimed to discover potential biomarkers for lung adenocarcinoma. These findings may offer new insights into the role of the gut microbiome in the prevention and treatment of lung adenocarcinoma. We used genome-wide association study (GWAS) summary statistics to assess the association between the gut microbiome and lung adenocarcinoma through two-sample MR analysis. Sensitivity analyses were performed to confirm the robustness of the findings. Reverse MR analysis and GWAS data integration were employed to identify potential genetic and therapeutic targets. Bioinformatics analysis and quantitative Real-Time PCR (qRT-PCR) were utilized to validate gene expression and explore the underlying mechanisms of key genes. Our analysis identified two bacterial taxa, Prevotella9 and Parabacteroides, as being causally associated with lung adenocarcinoma, both showing positive causal relationships. Sensitivity analyses confirmed the robustness of these associations. The reverse MR analysis revealed no evidence of reverse causality. GWAS data identified 15 genes (DNAH1, PDE10A, DOCK2, INSYN2B, DNAI3, SUOX, LINC01505, SULT4A1, NT5ELP, LINC02895, calcium/calmodulin dependent protein kinase 1D (CAMK1D), ENSG00000253557, BCAS3, C18orf63, MYO18B) that passed the summary-data-based MR test. The transcriptomic data revealed that five genes (CAMK1D, BCAS3, DNAH1, PDE10A, and C18orf63) were differentially expressed between lung adenocarcinoma patients and healthy individuals. Through qRT-PCR validation, the CAMK1D gene was markedly upregulated in lung adenocarcinoma cell lines, whereas BCAS3, DNAH1, PDE10A, and C18orf63 genes exhibit ed substantially reduced expression. Our study identified specific gut microbial taxa as risk factors for lung adenocarcinoma and proposes CAMK1D as a microbiota-related candidate biomarker and potential therapeutic target that may inform personalized treatment and drug development strategies in the future.

Background: Observational research and medical trials have suggested a connection between gut microbiota and glioblastoma, but it remains unclear if the relationship is causal. Method: A two-sample Mendelian randomization (MR) study was conducted by employing data from the MiBioGen consortium's largest genome-wide association study (n=18340) and the FinnGen consortium R8 release information (162 cases and 256,583 controls). Inverse variance weighted (IVW), weighted median estimator (WME), weighted model, MR-Egger, simple mode, and MR-PRESSO were used to determine the causal relationship between gut microbiota and glioblastoma. Reverse MR analysis was also performed on bacteria identified as causally related to glioblastoma. Results: Seven causal relationships were identified between genetic liability in the gut microbiota and glioblastoma, involving various bacterial families and genera. No significant causal effect was found on gut microbiota from glioblastoma, and no significant heterogeneity of instrumental variables (IVs) or horizontal pleiotropy was observed. Conclusion: A two-sample MR analysis reveals a causal association between the gut microbiota and glioblastoma, highlighting the need for more investigation to comprehend the processes behind this association.

BackgroundGut microbiota is associated with endometrial cancer (EC); however, the causal relationship remains unexplored. This study attempted to explore the relationship between gut microbiota and EC using Mendelian randomization (MR) methods.MethodsIn this two-sample MR analysis, we used MiBioGen’s gut microbiota data as the exposure and three datasets from European populations with EC as the outcome. The EC datasets included general EC, endometrioid histology, and non-endometrioid histology. Single nucleotide polymorphism (SNP) was used as the instrumental variable. Inverse variance weighted (IVW), multiplicative random effects IVW (MRE-IVW), Maximum likelihood (ML), MR Egger, MR-PRESSO, and the weighted median were used to perform MR analysis. Sensitivity analysis was conducted to assess the reliability of the results.ResultsIn this MR analysis of three EC datasets, specific gut microbiota were identified as potentially associated with different pathological types of EC. For general EC (ID: ebi-a-GCST006464), Family.Acidaminococcaceae (OR = 1.23, 95%CI: 1.02–1.48) and genus.Butyrivibrio (OR = 1.08, 95%CI: 1.01–1.16) were identified as risk factors, while genus.Ruminococcaceae UCG014 (OR = 0.82, 95%CI: 0.69–0.98) and genus.Turicibacter (OR = 0.84, 95%CI: 0.73–0.97) appeared to have protective effects. For endometrioid histology EC (ID: ebi-a-GCST006465), Family.Acidaminococcaceae (OR = 1.27, 95%CI: 1.01–1.59) and genus.Butyrivibrio (OR = 1.10, 95%CI: 1.01–1.19) were identified as risk factors, while several microbiota, including Family.Lactobacillaceae, genus.Coprococcus3, genus.Dorea, genus.Flavonifractor, genus.Lactobacillus, genus.Paraprevotella, and genus.Turicibacter, were identified as protective factors. For non-endometrioid histology EC (ID: ebi-a-GCST006466), Family.Rhodospirillaceae (OR = 1.41, 95%CI: 1.01–1.96) and genus.Peptococcus (OR = 1.43, 95%CI: 1.07–1.91) were identified as risk factors, while no significant protective factors were identified.ConclusionsThis two-sample MR study has identified gut microbiota with potential causal relationships with EC, varying by pathological type. These findings provide new insights into the pathogenesis of EC and suggest directions for future research on diagnosis and treatment strategies.

Background: Childhood obesity is characterized by metabolic dysregulation and unique gut microbiota profiles. Nevertheless, the comprehensive understanding of gut microbiota and metabolic dysregulation of childhood obesity remains unclear. Objectives: This study aimed to investigate the causal relationship of gut microbiota and childhood obesity and identify the blood metabolites as potential mediators. Methods: The exposure genome-wide association studies (GWAS) data were sourced from the GWAS Catalog, while the outcome GWAS data were obtained from the Early Growth Genetics (EGG) Consortium. The study used 473 types of gut microbiota, 233 types of blood metabolites, and childhood obesity from GWAS. We then performed two-sample Mendelian randomization (TSMR) and bidirectional Mendelian randomization (BDMR) analyses to explore the causal relationships between gut microbiota, blood metabolites, and childhood obesity. Additionally, we conducted multivariable Mendelian randomization (MVMR) and two-step Mendelian randomization (2SMR) to identify potential mediating blood metabolites in this process. Results: MR analysis identified 13 types of gut microbiota and 12 types of blood metabolites that were causally associated with childhood obesity. Furthermore, there was no strong evidence that genetically predicted childhood obesity had an effect on these gut microbiota and blood metabolites. Further, 2SMR analysis revealed that the association between K10 sp001941205 and childhood obesity was mediated by the Total cholesterol to total lipids ratio in medium VLDL, accounting for 2.53% (95%CI; 2.14%-2.92%) of the association. Similarly, the relationship between SM23-33 and childhood obesity was mediated by the Ratio of 22:6 docosahexaenoic acid to total fatty acids, which accounted for 4.07% (95%CI; 2.70%-5.44%) of the association. Conclusion: The present study is the first to investigate the causal relationships among 473 gut microbiota phenotypes, 233 blood metabolites, and childhood obesity through Mendelian randomization analysis, identifying 13 gut microbiota types with potential causal links to childhood obesity and suggesting that 2 blood metabolites may mediate these associations, thereby providing valuable insights for future intervention strategies aimed at addressing childhood obesity

Emerging evidence has underscored the complex link between gut microbiota and chemotherapy efficacy; however, establishing causality remains elusive due to confounding factors. This study, leveraging bidirectional two-sample Mendelian randomization (MR) analyses, explores the casual relationship between gut microbiota and chemotherapy efficacy. Utilizing genome-wide association study (GWAS) data from the MiBioGen consortium for gut microbiota and IEU Open GWAS for chemotherapy efficacy, we employed genetic variants as instrumental variables (IVs). The inverse variance weighted (IVW) method, weighted median estimator (WME), and MR-Egger regression method were applied, with sensitivity analyses ensuring robustness. Furthermore, we conducted reverse MR analyses between chemotherapy efficacy and identified significant gut microbial taxa. The results indicated that genus Butyricicoccus (OR = 3.7908, 95% CI: 1.4464-9.9350, P = 0.01), Dorea (OR = 3.3295, 95% CI: 1.2794-8.6643, P = 0.01), Hungatella (OR = 2.6284, 95% CI: 1.0548-6.5498, P = 0.04), and Turicibacter (OR = 2.5694, 95% CI: 1.0392-6.3526, P = 0.04) were positively associated with chemotherapy efficacy using the IVW method. Conversely, family Porphyromonadaceae (OR = 0.2283, 95% CI: 0.0699-0.7461, P = 0.01) and genus Eggerthella (OR = 0.4953, 95% CI: 0.2443-1.0043, P = 0.05) exhibited negative associations. WME demonstrated consistent results with IVW method only for genus Eggerthella (OR = 0.3343, 95% CI: 0.1298-0.8610, P = 0.02). No significant heterogeneity or horizontal pleiotropy was observed. Reverse MR analyses revealed no significant causal effect of chemotherapy on identified gut microbiota. This study sheds light on the intricate relationship between gut microbiota, with a particular emphasis on the genus Eggerthella, and chemotherapy efficacy, offering valuable insights for refining cancer treatment strategies.IMPORTANCEGlobal advancements in cancer treatment, particularly in chemotherapy, have notably decreased mortality rates in recent years. However, the correlation between gut microbiota and chemotherapy efficacy remains elusive. Our study, emphasizing the role of genus Eggerthella, represented a crucial advance in elucidating this intricate interplay. The identified associations offer potential therapeutic targets, contributing to global efforts for enhanced treatment precision and improved patient outcomes. Furthermore, our findings hold promise for personalized therapeutic interventions, shaping improved strategies in the ever-evolving landscape of cancer treatment.

Considerable evidence has unveiled a potential correlation between gut microbiota and spinal degenerative diseases. However, only limited studies have reported the direct association between gut microbiota and spinal stenosis. Hence, in this study, we aimed to clarify this relationship using a two-sample mendelian randomization (MR) approach. Data for two-sample MR studies was collected and summarized from genome-wide association studies (GWAS) of gut microbiota (MiBioGen, n = 13, 266) and spinal stenosis (FinnGen Biobank, 9, 169 cases and 164, 682 controls). The inverse variance-weighted meta-analysis (IVW), complemented with weighted median, MR-Egger, weighted mode, and simple mode, was used to elucidate the causality between gut microbiota and spinal stenosis. In addition, we employed mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and the MR-Egger intercept test to assess horizontal multiplicity. Cochran's Q test to evaluate heterogeneity, and "leave-one-out" sensitivity analysis to determine the reliability of causality. Finally, an inverse MR analysis was performed to assess the reverse causality. The IVW results indicated that two gut microbial taxa, the genus Eubacterium fissicatena group and the genus Oxalobacter, have a potential causal relationship with spinal stenosis. Moreover, eight potential associations between genetic liability of the gut microbiota and spinal stenosis were implied. No significant heterogeneity of instrumental variables or horizontal pleiotropy were detected. In addition, "leave-one-out" sensitivity analysis confirmed the reliability of causality. Finally, the reverse MR analysis revealed that no proof to substantiate the discernible causative relationship between spinal stenosis and gut microbiota. This analysis demonstrated a possible causal relationship between certain particular gut microbiota and the occurrence of spinal stenosis. Further studies focused on the mechanism of gut microbiota-mediated spinal stenosis can lay the groundwork for targeted prevention, monitoring, and treatment of spinal stenosis.

BackgroundObservational studies have suggested that hearing impairment (HI) was associated with the risk of falls, but it remains unclear if this association is of causal nature.MethodsA two-sample Mendelian randomization (MR) study was conducted to investigate the causal association between HI and falls in individuals of European descent. Summary data on the association of single nucleotide polymorphisms (SNPs) with HI were obtained from the hitherto largest genome-wide association study (GWAS) (n = 323,978), and statistics on the association of SNPs with falls were extracted from another recently published GWAS (n = 461,725). MR Steiger filtering method was applied to determine the causal direction between HI and falls. Inverse-variance weighted (IVW) method was employed as the main approach to analyze the causal association between HI and falls, whereas weighted median, simple mode, weighted mode, and MR-Egger methods were used as complementary analyses. The MR-Egger intercept test, the MR-PRESSO test, and Cochran's Q statistic were performed to detect the potential directional pleiotropy and heterogeneity, respectively. The odds ratio (OR) with 95% confidence intervals (CIs) was used to evaluate this association.ResultsA total of 18 SNPs were identified as valid instrumental variables in our two-sample MR analysis. The positive causality between HI and risk of falls was indicated by IVW [OR 1.108 (95% CI 1.028, 1.194), p = 0.007]. The sensitivity analyses yielded comparable results. The “leave-one-out” analysis proved that lack of a single SNP did not affect the robustness of our results. The MR-Egger intercept test exhibited that genetic pleiotropy did not bias the results [intercept = −2.4E−04, SE = 0.001, p = 0.832]. Cochran's Q test revealed no heterogeneity.ConclusionOur MR study revealed a causal association between genetically predicted HI and falls. These results provide further evidence supporting the need to effectively manage HI to minimize fall risks and improve quality of life.

BackgroundAccumulating evidence suggests that alterations in gut microbiota composition are associated with the hidradenitis suppurativa (HS). However, the causal association between gut microbiota and HS remain undetermined.MethodsWe performed a bidirectional two-sample Mendelian randomization (MR) analysis using genome-wide association study summary data of gut microbiota and hidradenitis suppurativa from the MiBioGen consortium which concluded 18,340 individuals analyzed by the MiBioGen Consortium, comprising 211 gut microbiota. HS data were acquired from strictly defined HS data collected by FinnGenbiobank analysis, which included 211,548 European ancestors (409 HS patients, 211,139 controls). The inverse variance weighted method (IVW), weighted median (WME), simple model, weighted model, weighted median, and MR-Egger were used to determine the changes of HS pathogenic bacterial taxa, followed by sensitivity analysis including horizontal pleiotropy analysis. The MR Steiger test evaluated the strength of a causal association and the leave-one-out method assessed the reliability of the results. Additionally, a reverse MR analysis was carried out to seek for possible reverse causality.ResultsBy combining the findings of all the MR steps, we identified four causal bacterial taxa, namely, Family XI, Porphyromonadaceae, Clostridium innocuum group and Lachnospira. The risk of HS might be positively associated with a high relative abundance of Clostridium innocuum group (Odds ratio, OR 2.17, p = 0.00038) and Lachnospira (OR 2.45, p = 0.017) but negatively associated with Family XI (OR 0.67, p = 0.049) and Porphyromonadaceae (OR 0.29, p = 0.014). There were no noticeable outliers, horizontal pleiotropy, or heterogeneity. Furthermore, there was no proof of reverse causation found in the reverse MR study.ConclusionThis study indicates that Clostridium innocuum group and Lachnospira might have anti-protective effect on HS, whereas Family XI and Porphyromonadaceae might have a protective effect on HS. Our study reveals that there exists a beneficial or detrimental causal effect of gut microbiota composition on HS and offers potentially beneficial methods for therapy and avoidance of HS.

BackgroundAlthough anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.MethodsA two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran’s Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10–4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.ResultsIn MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (ORfixed-effect IVW = 1.04; 95% CI 1.021–1.06; Pfixed-effect IVW = 4.3 × 10–5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a “leave-one-out” analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = − 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran’s Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the “primary bile acid biosynthesis” pathway (P = 0.008) and the “valine, leucine, and isoleucine biosynthesis” pathway (P = 0.03), were identified through metabolic pathway analysis.ConclusionThis study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

Proteins are involved in multiple biological functions. High-throughput technologies have allowed the measurement of thousands of proteins in population biobanks. In this study, we aimed to identify proteins related to Alzheimer’s disease, Parkinson’s disease, multiple sclerosis and amyotrophic lateral sclerosis by leveraging large-scale genetic and proteomic data.We performed a two-sample cis Mendelian randomization study by selecting instrumental variables for the abundance of >2700 proteins measured by either Olink or SomaScan platforms in plasma from the UK Biobank and the deCODE Health Study. We also used the latest publicly available genome-wide association studies for the neurodegenerative diseases of interest. The potentially causal effect of proteins on neurodegenerative diseases was estimated based on the Wald ratio.We tested 13 377 protein–disease associations, identifying 169 associations that were statistically significant (5% false discovery rate). Evidence of co-localization between plasma protein abundance and disease risk (posterior probability > 0.80) was identified for 61 protein–disease pairs, leading to 50 unique protein–disease associations. Notably, 23 of 50 protein–disease associations corresponded to genetic loci not previously reported by genome-wide association studies. The two-sample Mendelian randomization and co-localization analysis also showed that APOE abundance in plasma was associated with three subcortical volumes (hippocampus, amygdala and nucleus accumbens) and white matter hyper-intensities, whereas PILRA and PILRB abundance in plasma was associated with caudate nucleus volume.Our study provided a comprehensive assessment of the effect of the human proteome that is currently measurable through two different platforms on neurodegenerative diseases. The newly associated proteins indicated the involvement of complement (C1S and C1R), microglia (SIRPA, SIGLEC9 and PRSS8) and lysosomes (CLN5) in Alzheimer’s disease; the interleukin-6 pathway (CTF1) in Parkinson’s disease; lysosomes (TPP1), blood–brain barrier integrity (MFAP2) and astrocytes (TNFSF13) in amyotrophic lateral sclerosis; and blood–brain barrier integrity (VEGFB), oligodendrocytes (PARP1), node of Ranvier and dorsal root ganglion (NCS1, FLRT3 and CDH15) and the innate immune system (CR1, AHSG and WARS) in multiple sclerosis. Our study demonstrates how harnessing large-scale genomic and proteomic data can yield new insights into the role of the plasma proteome in the pathogenesis of neurodegenerative diseases.