Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects

Association between depression and vascular aging: a comprehensive analysis of predictive value and mortality risks

BACKGROUND

Depression is a significant global health concern, projected to become the leading disease burden. Vascular burden has been implicated in the pathogenesis of depression. Conversely, whether depression independently influences the process of vascular aging is unknown. This study aims to investigate the mutual relationship between vascular age and depression.

METHODS

Utilizing data from the National Health and Nutrition Examination Survey (NHANES), the study included 27,764 participants after exclusions. Depression was assessed using the Patient Health Questionnaire (PHQ-9). Vascular aging was assessed by estimated pulse wave velocity (ePWV) and the heart age/vascular age (HVA) based on Framingham Risk Score (FRS). The study employed weighted logistic regression and Cox proportional hazards models to analyze the association between vascular age and depression as well as its mortality risk. Mendelian randomization was utilized to explore the causal associations.

RESULTS

Individuals with depression exhibited a higher risk of an advanced vascular age over their chronological age. Mendelian randomization analysis indicated a causal relationship between depression and arterial stiffness. A significant association was found between vascular age and depression incidence with odds ratios ranging from 1.10 to 1.38. As vascular age increased, the risk of mortality in individuals with depression increased by 22 % and 46 %, respectively.

LIMITATIONS

The study design limits the exploration of the dynamic relationship between changes in vascular age and depression due to the single timepoint measurement.

CONCLUSION

This study highlights the bidirectional relationship between depression and vascular age. Vascular age is a significant biomarker for the risk and prognosis of depression, while depression may contribute to vascular aging, which underscores the importance of integrated strategies for managing both vascular health and depression.

Proteome-Wide Mendelian Randomisation Identifies Causal Links of Plasma Proteins With Periodontitis

OBJECTIVE

Periodontitis is a complex and multifactorial disease and it is challenging to decipher its underlying causes and mechanisms. This study attempted to explore potential circulating proteins in connection to periodontitis through proteome-wide Mendelian randomisation (MR).

METHODS

We analysed 1722 circulating proteins to identify prospective drug targets for tackling periodontitis, using the genomic dataset from the FinnGen study. Two-sample MR was conducted to evaluate the bidirectional relationship between circulating proteins and periodontitis risk. A dataset from the UK Biobank was used to validate the findings. Single-cell analysis was performed to assess the cellular expression of the identified proteins within gingival tissues.

RESULTS

MR analyses found that genetically predicted circulating levels of von Willebrand factor A domain-containing 1 (von Willebrand factor A domain containing 1 [VWA1], odds ratios: 0.94, 95% CI 0.92-0.97, P = 1.28 × 10-5) were inversely associated with periodontitis. In contrast, the level of growth differentiation factor 15 (growth differentiation factor 15 [GDF15], odds ratios: 1.05, 95% CI 1.02-1.07, P = 2.12 × 10-5) might be associated with an increased risk of periodontitis. Single-cell analysis indicated that VWA1 was primarily expressed in endothelial cells of healthy gingival tissues, while the main source of GDF15 was not derived from periodontal cells.

CONCLUSIONS

The present study suggests that certain plasma proteins like VWA1 and GDF15 may be potentially indicative of the risk and susceptibility to periodontitis. These proteins could possibly be the potential therapeutic targets for treating periodontitis, and further investigation is highly warranted.

Impact of elevated CO2 on soil microbiota: A meta-analytical review of carbon and nitrogen metabolism

In the face of 21st-century challenges driven by population growth and resource depletion, understanding the intricacies of climate change is crucial for environmental sustainability. This review systematically explores the interaction between rising atmospheric CO2 concentrations and soil microbial populations, with possible feedback effects on climate change and terrestrial carbon (C) cycling through a meta-analytical approach. Furthermore, it investigates the enzymatic activities related to carbon acquisition, gene expression patterns governing carbon and nitrogen metabolism, and metagenomic and meta-transcriptomic dynamics in response to elevated CO2 levels. The study reveals that elevated CO2 levels substantially influence soil microbial communities, increasing microbial biomass C and respiration rate by 15 % and upregulating genes involved in carbon and nitrogen metabolism by 12 %. Despite a 14 % increase in C-acquiring enzyme activity, there is a 5 % decrease in N-acquiring enzyme activity, indicating complex microbial responses to CO2 changes. Additionally, fungal marker ratios increase by 14 % compared to bacterial markers, indicating potential ecosystem changes. However, the current inadequacy of data on metagenomic and meta-transcriptomic processes underscores the need for further research. Understanding soil microbial feedback mechanisms is crucial for elucidating the role of rising CO2 levels in carbon sequestration and climate regulation. Consequently, future research should prioritize a comprehensive elucidation of soil microbial carbon cycling, greenhouse gas emission dynamics, and their underlying drivers.

A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and Uncorrelated Pleiotropic Effects

We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions. In simulations, our proposed approaches demonstrate robustness to the violations, delivering valid estimates, and interpretable type-I errors and statistical power. This increases the practical applicability of the models. We applied the proposed approaches to evaluate the causal effect of fetal hemoglobin (HbF) on the vaso-occlusive crisis and acute chest syndrome (ACS) events in patients with sickle cell disease (SCD) and revealed the causal relation between HbF and ACS events in these patients. We also developed a user-friendly Shiny web application to facilitate researchers' exploration of causal relations.

Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome

Metabolic syndrome (MetS) is a complex hereditary condition comprising various metabolic traits as risk factors. Although the genetics of individual MetS components have been investigated actively through large-scale genome-wide association studies, the conjoint genetic architecture has not been fully elucidated. Here, we performed the largest multivariate genome-wide association study of MetS in Europe (nobserved = 4,947,860) by leveraging genetic correlation between MetS components. We identified 1,307 genetic loci associated with MetS that were enriched primarily in brain tissues. Using transcriptomic data, we identified 11 genes associated strongly with MetS. Our phenome-wide association and Mendelian randomization analyses highlighted associations of MetS with diverse diseases beyond cardiometabolic diseases. Polygenic risk score analysis demonstrated better discrimination of MetS and predictive power in European and East Asian populations. Altogether, our findings will guide future studies aimed at elucidating the genetic architecture of MetS.

Causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity

The causative mechanisms underlying the genetic relationships of neurodegenerative diseases with epigenetic aging and human longevity remain obscure. We aimed to detect causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity. We obtained large-scale genome-wide association study summary statistics data for four measures of epigenetic age (GrimAge, PhenoAge, IEAA, and HannumAge) (N = 34,710), multivariate longevity (healthspan, lifespan, and exceptional longevity) (N = 1,349,462), and for multiple neurodegenerative diseases (N = 6618-482,730), including Lewy body dementia, Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis. Main analyses were conducted using multiplicative random effects inverse-variance weighted Mendelian randomization (MR), and conditional/conjunctional false discovery rate (cond/conjFDR) approach. Shared genomic loci were functionally characterized to gain biological understanding. Evidence showed that AD patients had 0.309 year less in exceptional longevity (IVW beta = -0.309, 95% CI: -0.38 to -0.24, p = 1.51E-19). We also observed suggestively significant causal evidence between AD and GrimAge age acceleration (IVW beta = -0.10, 95% CI: -0.188 to -0.013, p = 0.02). Following the discovery of polygenic overlap, we identified rs78143120 as shared genomic locus between AD and GrimAge age acceleration, and rs12691088 between AD and exceptional longevity. Among these loci, rs78143120 was novel for AD. In conclusion, we observed that only AD had causal effects on epigenetic aging and human longevity, while other neurodegenerative diseases did not. The genetic overlap between them, with mixed effect directions, suggested complex shared genetic etiology and molecular mechanisms.

Causal relationships between gut microbiota and polycystic ovarian syndrome: A bidirectional Mendelian randomization study

INTRODUCTION

Previous studies have established a link between gut microbiota and polycystic ovary syndrome (PCOS), but little is known about their precise causal relationship. Therefore, this study aims to explore whether there are precise causal relationships between gut microbiota and PCOS.

MATERIAL AND METHODS

We performed a bidirectional two-sample Mendelian randomization (MR) analysis. Datasets were from the largest published meta-analysis on gut microbiota composition and the FinnGen cohort of the IEU Open Genome-Wide Association Study Project database. Inverse variance weighted (IVW), MR-Egger, constrained maximum likelihood-based Mendelian randomization, weighted median, weighted mode, and simple mode were used. Cochran's Q and MR-Egger intercept tests were employed to measure the heterogeneity.

RESULTS

A total of 211 gut microbiota taxa were identified in MR analysis. Nine taxa of bacteria, including Alphaproteobacteria (0.55, 0.30-0.99, p = 0.04), Bacilli (1.76, 1.07-2.91, p = 0.03), Bilophila (0.42, 0.23-0.77, p < 0.01), Blautia (0.16, 0.03-0.79, p = 0.02), Burkholderiales (2.37, 1.22-4.62, p = 0.01), Candidatus Soleaferrea (0.65, 0.43-0.98, p = 0.04), Cyanobacteria (0.51, 0.31-0.83, p = 0.01), Holdemania (0.53, 0.35-0.81, p < 0.01), and Lachnospiraceae (1.86, 1.04-3.35, p = 0.03), were found to be associated with PCOS in the above MR methods included at least IVW method. Cochran's Q statistics and MR-Egger intercept test suggested no significant heterogeneity. In addition, 69 taxa were shown significant for at least the IVW method in reverse MR analysis, of these, 25 had a positive correlation, and 37 had a negative correlation. Additionally, Alphaproteobacteria and Lachnospiraceae (0.95, 0.91-0.98, p < 0.01; 0.97, 0.94-0.99, p = 0.02, respectively) were shown a bidirected causally association with PCOS.

CONCLUSIONS

Our study provides evidence of the bidirectional causal association between gut microbiota and PCOS from a genetic perspective.

Cysteine cathepsins and autoimmune diseases: A bidirectional Mendelian randomization

Cysteine cathepsins are proteolytic enzymes crucial in various physiological and pathological processes, primarily operating within lysosomes. Their functions include protein degradation, immune system regulation, and involvement in various diseases. While some cysteine cathepsins play important roles in the immune system, their connection to autoimmune diseases remains unclear. This study proposes using Mendelian randomization to explore the causal relationship between cysteine cathepsins and autoimmune diseases. Single nucleotide polymorphisms (SNPs) for cysteine cathepsins were obtained from a publicly available genome-wide association study (GWAS) dataset, while outcome SNP data were sourced from 10 separate GWAS datasets. Mendelian randomization (MR) analysis employed the Wald ratio (WR) and inverse variance weighted (IVW) approach as primary methods, supplemented by the weighted median and MR-Egger methods. Heterogeneity was assessed using Cochran Q test, and sensitivity analysis was conducted using the MR-PRESSO method. The association strength between exposure and outcome was evaluated using odds ratios (OR) with 95% confidence intervals (CI). The study identified a potential positive correlation between elevated cathepsin B and psoriasis (Wald ratio OR = 1.449, 95% CI: 1.053-1.993, P = .0227). Elevated cathepsin F was potentially linked to ulcerative colitis (WR OR = 1.073, 95% CI: 1.021-1.127, P = .0056), ankylosing spondylitis (WR OR = 1.258, 95% CI: 1.082-1.463, P = .0029), and primary biliary cholangitis(PBC) (WR OR = 1.958, 95% CI: 1.326-2.889, P = .0007). Conversely, cathepsin H appeared protective against celiac disease (WR OR = 0.881, 95% CI: 0.838-0.926, P = 6.5e-7), though elevated levels may increase the risk of type 1 diabetes (IVW OR = 1.121, 95% CI: 1.053-1.194, P = .0003) and PBC (WR OR = 1.792, 95% CI: 1.062-3.024, P = .0288). Cathepsin Z was also associated with an increased risk of type 1 diabetes (IVW OR = 1.090, 95% CI: 1.006-1.181, P = .0349). The MR analysis suggests potential risks of cathepsin B with psoriasis, cathepsin F with ulcerative colitis, ankylosing spondylitis, and PBC, and cathepsin Z with type 1 diabetes. Conversely, cathepsin H may protect against celiac disease but could increase the risk of type 1 diabetes and PBC.

Serum iron status and the risk of female infertility in European populations: A two-sample Mendelian randomization study

The relationship between iron status and female infertility has been observed in several studies, yet its causal nature remains ambiguous. We employed univariate Mendelian randomization (MR) analyses to explore the potential causal connection between these 2 factors. For our analysis, genetic instrumental variables pertaining to iron status were selected using data from the Iron Status Genetics Consortium, encompassing 48,972 individuals of European descent from 19 cohorts (11 discovery and 8 replication). For female infertility data, we referred to FinnGen Consortium Release 9, which includes 11,442 cases and 107,564 controls. Our MR approach utilized both a conservative strategy (involving single nucleotide polymorphisms pertinent to general iron status) and a liberal strategy (encompassing single nucleotide polymorphisms linked to any iron status indicator). The conservative approach relied on inverse variance-weighted methods, whereas the liberal strategy integrated inverse variance weighted with MR-Egger regression, the weighted median approach, and simple mode techniques. The conservative strategy did not reveal a significant link between iron status and female infertility risk. Conversely, the liberal strategy indicated a positive correlation specifically between serum iron levels and female infertility risk (odds ratio from MR: 1.225; 95% confidence interval: 1.064-1.410; P = .030), while no significant associations were found for other iron indicators (P > 0.05). Our MR investigation suggests a potential positive association between serum iron levels and the risk of female infertility, while other iron markers do not appear to significantly influence this risk. These findings highlight the need for further research into the possible connection between serum iron status and female infertility risk.

Exploring the potential link between gut microbiota and chronic kidney disease in causality: A 2-sample Mendelian randomization study

Increasing evidence indicates a significant correlation between gut microbiota (GM) and susceptibility to chronic kidney disease (CKD). However, causal relationship presence remains uncertain. Mendelian randomization (MR) was applied to evaluate potential causal relation from GM to CKD. Genomic association analysis aggregates publicly online databases, utilizing Genome-Wide Association Study (GWAS) database focused on GM and CKD. For examination of potential causal connection from GM to CKD, a 2-way, 2-sample Mendelian randomization (MR) method was applied. Sensitivity analyses were utilized to scrutinize for heterogeneity, horizontal pleiotropy, MR outcomes resilience. Result from inverse variance weighting (IVW) method revealed that 10 microbiotas such as Porphyromonadaceae (OR = 1.351, 95% CI: 1.114-1.638, P = .002), Dorea (OR = 1.236, 95% CI: 1.040-1.468, P = .016), Ruminococcus torques group (OR = 1.290, 95% CI: 1.035-1.608, P = .024) are potential CKD risk factors. Five microbiotas, including the Prevotellaceae (OR = 0.814, 95% CI: 0.719-0.922, P = .001) are potential CKD protective factors. Sensitivity analyses reveal no horizontal pleiotropy or heterogeneity. Additionally, reverse MR results unveiled potential relation between CKD and disorders in 3 microbiotas, including Senegalimassilia. According to the investigation, MR method was employed to delve into reciprocal causal connection from GM to CKD. Our findings identified 15 types of GM causally linked to CKD, as well as CKD demonstrating causal associations with 3 types of GM. Further exploration of these associated GM types is hopeful to raise novel insights, for CKD preventing and early monitoring.

Unraveling Shared and Unique Genetic Causal Relationship Between Gut Microbiota and Four Types of Uterine-Related Diseases: Bidirectional Mendelian Inheritance Approaches to Dissect the "Gut-Uterus Axis"

BACKGROUND

The gut microbiota has emerged as a pivotal factor in the etiology of uterine-related diseases. This study aims to elucidate the genetic causal link between gut microbiota composition and these conditions, focusing on the systemic impact and uterine pathology to better understand the "Gut-Uterus Axis."

METHODS

We utilized pooled data from two different GWAS databases, including data from 209 gut microbiota traits and data from four uterus-related diseases. Bidirectional Mendelian Randomization (MR) approaches, incorporating Bayesian weighting and traditional inverse variance weighting (IVW) methods, were employed to explore causal relationships. The robustness of findings was ensured through sensitivity analyses, outlier testing, and MR-PRESSO analysis.

RESULTS

Seventeen significant associations were identified between gut microbiota traits and uterine-related diseases, suggesting potential causal links. These associations were consistent across sensitivity analyses, affirming the reliability of our results. Conversely, reverse MR analyses did not reveal statistically significant associations between uterine diseases and bacterial traits, indicating a unidirectional influence of gut microbiota on uterine health. These findings highlight the complex interplay within the "Gut-Uterus Axis."

CONCLUSION

This research establishes a causal relationship between gut microbiota and uterine diseases, advocating for targeted interventions to mitigate associated risks. It underscores the interconnectedness of gut and reproductive health, promoting a holistic approach to management and treatment within the "Gut-Uterus Axis".

Hypertensive disorders of pregnancy and stroke: a univariate and multivariate Mendelian randomization study

Background

Hypertensive disorders of pregnancy (HDP) are associated with an increased risk of stroke later in life in multiparous women. However, causality of these associations remains unclear. This study employed 2-sample univariate and multivariate Mendelian randomization (MR) to assess the causal connection between HDP and stroke.

Methods

Genetic variants for HDP and two subtypes were identified from recent large-scale genome-wide association studies and the FinnGen consortium. Stroke summary data were obtained from the MEGASTROKE consortium. The primary analytical approach for univariate MR was the inverse variance weighting method. Sensitivity analyses incorporated methods such as MR-Egger regression, weighted median, and maximum likelihood to ascertain the robustness of the results. Additionally, multivariable MR analyses were conducted to account for potential associative effects of hypertension and type 2 diabetes.

Results

Genetically predicted HDP was associated with a high risk of large artery atherosclerosis (odds ratio [OR]=1.50, 95% confidence interval [CI]: 1.17-1.91, P=1.13×10-3) and small vessel stroke (OR=1.29, 95% CI: 1.20-1.50, P=1.52×10-3). HDP may also correlate with ischemic stroke (OR=1.13, 95% CI: 1.04-1.23, P=4.99×10-3) and stroke (OR=1.11, 95% CI: 1.03-1.20, P=8.85×10-3). An elevated risk of small vessel stroke (OR=1.20, 95% CI: 1.01-1.43, P=3.74×10-2) and large artery atherosclerosis (OR=1.22, 95% CI: 1.01-1.47, P=4.07×10-2) may be related with genetically predicted susceptibility to gestational hypertension. Genetically predicted susceptibility to preeclampsia or eclampsia may be associated with an increased risk of stroke (OR = 1.10, 95% CI: 1.02-1.19, P = 1.16×10-2) and ischemic stroke (OR = 1.10, 95% CI: 1.02-1.20, P = 1.84×10-2). Type 2 diabetes mellitus and hypertension were identified as significant factors contributing to the association between HDP and stroke.

Conclusions

This study provides genetic evidence supporting an association between HDP and increased stroke risk bolstering HDP as a cerebrovascular risk factor.

Exploring the association between skin microbiota and inflammatory skin diseases: a two-sample Mendelian randomization analysis

Dysbiosis in the skin microbiome is closely associated with various inflammatory skin diseases. However, current research on the causal relationship between the skin microbiome and inflammatory skin diseases lacks comprehensive and detailed investigation. We used a two-sample Mendelian randomization (MR) approach to explore associations between the skin microbiome and seven inflammatory skin diseases, including acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria. The GWAS summary data for the skin microbiome was derived from 647 participants in two German population-based cohorts, and for the inflammatory skin diseases, they were sourced from the FinnGen consortium. Our primary MR analysis method was the inverse variance weighted (IVW) method, complemented by alternatives like MR-Egger regression, weighted median estimation, and constrained maximum likelihood. Sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and MR-PRESSO outlier detection, were conducted to validate and stabilize our findings. We identified significant causal relationships between the skin microbiome and seven inflammatory skin diseases: acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria, with 7, 6, 9, 1, 7, 4, and 7 respective causal relationships for each disease. These relationships comprise 20 protective and 14 risk causal relationships. We applied the false discovery rate correction to these results. Sensitivity analysis revealed no significant pleiotropy or heterogeneity. Our study revealed both beneficial and detrimental causal relationships between diverse skin microbiota and inflammatory skin diseases. Additionally, the ecological niche of the skin microbiome was crucial to its functional impact. This research provided new insights into how skin microbiota impacted skin diseases and the development of therapeutic strategies.

Multi-Ancestry Causal Association between Rheumatoid Arthritis and Interstitial Lung Disease: A Bidirectional Two-Sample Mendelian Randomization Study

Background: Rheumatoid arthritis (RA) is associated with diverse extra-articular manifestations, including interstitial lung disease (ILD). No previous studies have examined the bidirectional relationship between RA and ILD using the Mendelian randomization (MR) analyses. Therefore, we aimed to investigate this subject using a two-sample bidirectional MR method. Methods: We performed bidirectional two-sample MR using summary statistics from genome-wide association studies (GWASs). The data are publicly available, de-identified, and from European (EUR) and East Asian (EAS) ancestries. Results: A total of 474,450 EUR participants and 351,653 EAS participants were included for either forward or reverse MR analysis. In our primary analysis, we found significant evidence of an increased risk of ILD associated with RA among individuals of EUR ancestry (ORMR-cML = 1.08; 95% confidence interval [CI] = 1.03-1.14; p = 0.003) and EAS ancestry (ORMR-cML = 1.37; 95% CI = 1.23-1.54; p < 0.001). Additionally, the reverse MR showed significant evidence of an increased risk of RA associated with ILD among those of EUR ancestry (ORMR-cML = 1.12; 95% CI = 1.05-1.19; p < 0.001). However, only one instrumental variable was selected in the EAS ILD GWAS, and there was no increased risk of RA associated with ILD in those of EAS ancestry (ORMR-cML = 1.02; 95% CI = 0.91-1.14; p = 0.740). Conclusions: Our findings indicate that RA and ILD have a bidirectional causal inference when using the MR analysis of GWAS datasets. The findings are only relevant for genetic predisposition; thus, further research is needed to determine the impact of non-genetic predispositions.

Association between brain resting-state functional activities and migraine: a bidirectional mendelian randomization study

Researchers have conducted extensive research on the correlation between brain resting-state functional activities (RSFA) and migraine. However, we still do not fully understand the exact nature of the causal relationship between these RSFA and migraine. We conducted a bidirectional two-sample Mendelian randomization (MR) study to investigate the causal association between migraine and RSFA. We gathered summary statistics from genome-wide association studies for 191 resting-state functional magnetic resonance imaging phenotypes. We employed various analytical methods for bidirectional two-sample MR analyses. This included inverse variance weighted, weighted median, MR Egger, and the constrained maximum likelihood approaches. We also conducted pleiotropy and heterogeneity analyses to evaluate the robustness and reliability. We found the functional connectivity between the default mode and the central executive network (OR = 1.39, p = 4.77 × 10-4, FDR corrected p value = 0.040) and the intensity of spontaneous brain activity in the calcarine or lingual gyrus within the visual network (OR = 0.74, p = 5.94 × 10-4, FDR corrected p value = 0.040) having a causal effect on the risk of migraine. Our MR analysis provided genetic support for these networks, which may play an important role in influencing migraine susceptibility.

Systematic evaluation of multifactorial causal associations for Alzheimer's disease and an interactive platform MRAD developed based on Mendelian randomization analysis

Alzheimer's disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.

Effects of multiple fluid intake on Urolithiasis by mendelian randomization study

Urolithiasis, a common and recurrent condition, imposes a significant global health burden. This study investigates the causal relationship between various types of fluid intake and urolithiasis using univariable and multivariable Mendelian randomization (MR) approaches. Genetic data for urolithiasis were obtained from the FinnGen Consortium (n = 400,681), while genetic measures for nine fluid types were sourced from meta-analyses of genome-wide association studies (GWAS). The analysis revealed that genetically predicted coffee intake (OR: 0.571, 95% confidence interval (CI): 0.341-0.958, p = 0.034) and ground coffee intake (OR: 0.219, 95% CI: 0.005-0.923, p = 0.038) were protective factors for upper urinary calculi. Additionally, a standard deviation (SD) increase in tea intake was associated with a 53% reduction in risk (OR: 0.473, 95% CI: 0.320-0.700, p < 0.001). Conversely, milk intake (OR: 1.072, 95% CI: 1.018-1.130) and alcohol intakefrequency (OR: 1.304, 95% CI: 1.083-1.570, p = 0.005) increased the risk of upper urinary calculi. These findings highlight the association of tea with reduced risk of urolithiasis, instead for milk and alcohol intake, in formulating prevention strategies for urolithiasis.

A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization

The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the "no horizontal pleiotropy" assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.

Gut microbiota causally impacts adrenal function: a two-sample mendelian randomization study

Some studies have reported that the gut microbiota can influence adrenal-related hormone levels. However, the causal effects of the gut microbiota on adrenal function remain unknown. Therefore, we employed a two-sample Mendelian randomization (MR) study to systematically investigate the impact of gut microbiota on the function of different regions of the adrenal gland. The summary statistics for gut microbiota and adrenal-related hormones used in the two-sample MR analysis were derived from publicly available genome-wide association studies (GWAS). In the MR analysis, inverse variance weighting (IVW) was used as the primary method, with MR-Egger, weighted median, and cML-MA serving as supplementary methods for causal inference. Sensitivity analyses such as the MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were used to assess pleiotropy and heterogeneity. We identified 27 causal relationships between 23 gut microbiota and adrenal function using the IVW method. Among these, Sellimonas enhanced the function of the adrenal cortex reticularis zone (beta = 0.008, 95% CI: 0.002-0.013, P = 0.0057). The cML-MA method supported our estimate (beta = 0.009, 95% CI: 0.004-0.013, P = 2 × 10- 4). Parasutterella, Sutterella, and Anaerofilum affect the functioning of different regions of the adrenal gland. Notably, pleiotropy was not observed. Our findings revealed that the gut microbiota is causally associated with adrenal function. This enhances our understanding of the gut-microbiota-brain axis and provides assistance in the early diagnosis and treatment of adrenal-related diseases in clinical practice.

The causality between rheumatoid arthritis and postural deformities: bidirectional Mendelian randomization study and mediation analysis

Background

To better understand the preventive or therapeutic clinical interventions that may be supported by the association between rheumatoid arthritis (RA) and postural deformities including hallux valgus, flat foot, and scoliosis, this study was conducted using Mendelian randomization (MR) analysis. It aimed to investigate whether RA is causally associated with postural deformities in European populations.

Methods

Summary-level data on RA and postural deformities were obtained from the IEU OpenGWAS project and Finngen database, respectively. LDSC regression analysis was conducted to assess the genetic correlation between these diseases. The inverse variance weighting (IVW) method was employed as the primary approach for two-sample MR analyses to evaluate causality. Supplementary methods included MR-Egger, maximum likelihood, weighted median, and cML-MA. To test for potential horizontal pleiotropy, we performed the MR-Egger intercept test, cML-MA, and secondary analyses after excluding confounders. Additionally, mediation analyses were conducted using two-step MR.

Results

The IVW method revealed RA to be causally associated with hallux valgus (OR 1.132, 95% CI 1.087-1.178, P < 0.001) and flat foot (OR 1.197, 95% CI 1.110-1.291, P < 0.001). Among postural deformities, hallux valgus was causally associated with flat foot (OR 1.823, 95% CI 1.569-2.119, P < 0.001) and scoliosis (OR 1.150, 95% CI 1.027-1.287, P < 0.05). No significant horizontal pleiotropy was detected. Moreover, mediation analyses indicated that hallux valgus mediates the effect of RA on flat foot (mediation effect 0.024, 95% CI 0.005-0.044, P < 0.05), with a mediation proportion of 41.31%.

Conclusion

These findings indicate a potential causal association between genetically predicted RA and both hallux valgus and flat foot. Furthermore, hallux valgus serves as a mediator in the pathway from RA to flat foot. This underscores the importance of early screening and preventive treatment of foot deformities in RA patients. Further research is necessary to determine the applicability of these findings in non-European populations.

Gut microbiota and interstitial cystitis: exploring the gut-bladder axis through mendelian randomization, biological annotation and bulk RNA sequencing

Background

Several observational studies have indicated an association between interstitial cystitis and the composition of the gut microbiota; however, the causality and underlying mechanisms remain unclear. Understanding the link between gut microbiota and interstitial cystitis could inform strategies for prevention and treatment.

Methods

A two-sample Mendelian randomization analysis was conducted using published genome-wide association study summary statistics. We employed inverse variance weighted, weighted mode, MR-Egger, weighted median, simple mode, and cML-MA methods to investigate the causal relationship between gut microbiota and interstitial cystitis. Sensitivity analysis was performed to validate the results. Relevant gut microbiota was examined through reverse MR. Single nucleotide polymorphisms were annotated using FUMA to identify genes associated with these genetic variants, thereby revealing potential host gene-microbiota associations in interstitial cystitis patients.

Results

Eight bacterial taxa were identified in our analysis as associated with interstitial cystitis. Among these, Butyricimonas, Coprococcus, Lactobacillales, Lentisphaerae, and Bilophila wadsworthia were positively correlated with interstitial cystitis risk, while taxa such as Desulfovibrio piger, Oscillibacter unclassified and Ruminococcus lactaris exhibited protective effects against interstitial cystitis. The robustness of these associations was confirmed through sensitivity analyses. Reverse MR analysis did not reveal evidence of reverse causality. Single nucleotide polymorphisms were annotated using FUMA and subjected to biological analysis. Seven hub genes (SPTBN1, PSME4, CHAC2, ERLEC1, ASB3, STAT5A, and STAT3) were identified as differentially expressed between interstitial cystitis patients and healthy individuals, representing potential therapeutic targets.

Conclusion

Our two-sample Mendelian randomization study established a causal relationship between gut microbiota and interstitial cystitis. Furthermore, our identification of a host gene-microbiota association offers a new avenue for investigating the potential pathogenesis of interstitial cystitis and suggests avenues for the development of personalized treatment strategies.

Identifying genetic variants associated with chromatin looping and genome function

Here we present a comprehensive HiChIP dataset on naïve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants. Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the RNASET2 locus). Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying previously uncharacterized QTLs linked to cell-specific gene expression and connectivity.

Mendelian randomization analysis reveals causal effects of blood lipidome on gestational diabetes mellitus

BACKGROUND

Observational studies have revealed associations between maternal lipid metabolites and gestational diabetes mellitus (GDM). However, whether these associations are causal remain uncertain.

OBJECTIVE

To evaluate the causal relationship between lipid metabolites and GDM.

METHODS

A two-sample Mendelian randomization (MR) analysis was performed based on summary statistics. Sensitivity analyses, validation analyses and reverse MR analyses were conducted to assess the robustness of the MR results. Additionally, a phenome-wide MR (Phe-MR) analysis was performed to evaluate potential side effects of the targeted lipid metabolites.

RESULTS

A total of 295 lipid metabolites were included in this study, 29 of them had three or more instrumental variables (IVs) suitable for sensitivity analyses. The ratio of triglycerides to phosphoglycerides (TG_by_PG) was identified as a potential causal biomarker for GDM (inverse variance weighted (IVW) estimate: odds ratio (OR) = 2.147, 95% confidential interval (95% CI) 1.415-3.257, P = 3.26e-4), which was confirmed by validation and reverse MR results. Two other lipid metabolites, palmitoyl sphingomyelin (d18:1/16:0) (PSM(d18:1/16:0)) (IVW estimate: OR = 0.747, 95% CI 0.583-0.956, P = 0.021) and triglycerides in very small very low-density lipoprotein (XS_VLDL_TG) (IVW estimate: OR = 2.948, 95% CI 1.197-5.215, P = 0.015), were identified as suggestive potential biomarkers for GDM using a conventional cut-off P-value of 0.05. Phe-MR results indicated that lowering TG_by_PG had detrimental effects on two diseases but advantageous effects on the other 13 diseases.

CONCLUSION

Genetically predicted elevated TG_by_PG are causally associated with an increased risk of GDM. Side-effect profiles indicate that TG_by_PG might be a target for GDM prevention, though caution is advised due to potential adverse effects on other conditions.

Causal effects of air pollutants on lung function and chronic respiratory diseases: a Mendelian randomization study

Objectives

Our study aims to clarify the causality between air pollutants and lung function, chronic respiratory diseases, and the potential mediating effects of inflammatory proteins.

Method

We employed Mendelian Randomization (MR) analysis with comprehensive instrumental variables screening criteria to investigate the effects of air pollutants on lung function and chronic lung diseases. Our study incorporated genetic instruments for air pollutants, ensuring F-statistics above 20.86. A total of 18 MR analyses were conducted using the inverse-variance weighted approach, along with heterogeneity and pleiotropy tests to validate the results. Mediated MR analysis was utilized to evaluate the inflammatory proteins mediating the effects of air pollutants.

Result

MR analysis demonstrated significant causal interactions of particulate matter 2.5 (PM2.5), PM10, and Nitrogen dioxide (NO2) with lung function decline. Specifically, PM10 negatively affected forced expiratory volume in one second (FEV1) (OR: 0.934, 95% CI: 0.904-0.965, p = 4.27 × 10-5), forced vital capacity (FVC) (OR: 0.941, 95% CI: 0.910-0.972, p = 2.86 × 10-4), and FEV1/FVC (OR: 0.965, 95% CI: 0.934-0.998, p = 0.036). PM2.5 and NO2 were identified as potential risk factors for impairing FEV1 (OR: 0.936, 95% CI: 0.879-0.998, p = 0.042) and FEV1/FVC (OR: 0.943, 95% CI: 0.896-0.992, p = 0.024), respectively. For chronic respiratory diseases, PM2.5 and NO2 were associated with increased COPD incidence (OR: 1.273, 95% CI: 1.053-1.541, p = 0.013 for PM2.5; OR: 1.357, 95% CI: 1.165-1.581, p = 8.74 × 10-5 for NO2). Sensitivity analyses confirmed the robustness of these findings, with no significant heterogeneity or horizontal pleiotropy detected.

Conclusion

Our study ascertained the causal correlations of air pollutants with lung function and COPD, emphasizing the importance of reducing air pollution. Interleukin-17A mediates the reduction of FEV1 and FVC by PM10, revealing potential therapeutic targets.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics

Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.

Multidimensional plasma lipid composition and its causal association with type 2 diabetes mellitus: A Mendelian randomization study

BACKGROUND AND AIM

Recent research extends our knowledge of plasma lipid species, building on established links between serum lipid levels and Type 2 Diabetes Mellitus (T2DM) risk. Identifying the causal roles of these lipid species is key to improving T2DM risk assessment.

METHODS AND RESULTS

This study employs Mendelian randomization (MR) to investigate the causal relationship between 179 lipid species across 13 lipid categories and T2DM. Summary-level data were sourced from genome-wide association studies. The primary analytical methods included the inverse variance weighted (IVW) approach and the Wald ratio, complemented by a series of sensitivity analyses to ensure the robustness of results. The IVW analysis reveals a significant causal association between elevated levels of ceramide (d40:2) (OR = 1.071, 95% CI 1.034-1.109, P = 1.36 × 10-4), sphingomyelin (d38:1) (OR = 1.052, 95% CI 1.028-1.077, P = 1.80 × 10-5), and triacylglycerol (56:8) (OR = 1.174, 95% CI 1.108-1.243, P = 4.65 × 10-8), and an increased risk of T2DM. Conversely, Wald ratio analysis indicates that higher levels of phosphatidylcholine (O-16:1_16:0) (OR = 0.928, 95% CI 0.892-0.966, P = 2.37 × 10-4), phosphatidylcholine (O-16:1_20:4) (OR = 0.932, 95% CI 0.897-0.967, P = 2.37 × 10-4), and phosphatidylcholine (O-18:2_20:4) (OR = 0.872, 95% CI 0.812-0.935, P = 1.24 × 10-4) are significantly associated with a reduced risk of T2DM. Furthermore, suggestive causal evidence for 22 additional lipid species was identified.

CONCLUSIONS

This MR study establishes a causal relationship between specific lipid classes in modulating the risk of T2DM. It offers new insights for risk assessment and potential therapeutic targets in T2DM.

High bioavailable testosterone levels increase the incidence of isolated REM sleep behavior disorder: Results from multivariable and network Mendelian randomization analysis

OBJECTIVES

To explore the causal relationships between sex hormone levels and incidence of isolated REM sleep behavior disorder (iRBD).

METHODS

In our study, we utilized Genome-Wide Association Studies (GWAS) data for iRBD, including 9447 samples with 1061 cases of iRBD provided by the International RBD Study Group. Initially, we conducted a two-sample univariate MR analysis to explore the impact of sex hormone-related indicators on iRBD. This was followed by the application of multivariable MR methods to adjust for other hormone levels and potential confounders. Finally, we undertook a network MR analysis, employing brain structure Magnetic Resonance Imaging (MRI) characteristics as potential mediators, to examine whether sex hormones could indirectly influence the incidence of iRBD by affecting brain structure.

RESULTS

Bioavailable testosterone (BioT) is an independent risk factor for iRBD (Odds Ratio [95 % Confidence Interval] = 2.437 [1.308, 4.539], P = 0.005, corrected-P = 0.020), a finding that remained consistent even after adjusting for other sex hormone levels and potential confounders. Additionally, BioT appears to indirectly increase the risk of iRBD by reducing axial diffusivity and increasing the orientation dispersion index in the left cingulum and cingulate gyrus.

CONCLUSIONS

Our research reveals that elevated levels of BioT contribute to the development of iRBD. However, the specific impact of BioT on different sexes remains unclear. Furthermore, high BioT may indirectly lead to iRBD by impairing normal pathways in the left cingulum and cingulate gyrus and fostering abnormal pathway formation.

Association between gut microbiota and acute pancreatitis: a bidirectional Mendelian randomization study

BACKGROUND AND AIM

The dysbiosis of gut microbiota has been reported in acute pancreatitis. However, the direction and magnitude between host microbiota and pancreas remains to be established. This study investigated the association between gut microbiota and acute pancreatitis using Mendelian randomization (MR) methods.

METHODS

Summary statistics of gut microbiota abundance and acute pancreatitis were extracted from genome-wide association studies (GWAS). The two-sample bidirectional MR design was employed to assess genetic association between the microbiota and pancreatitis, followed by a comprehensive sensitivity analysis to verify the robustness of the results.

RESULTS

Seven microbiota taxa have been identified as significantly associated with the development of pancreatitis. Host genetic-driven order Bacteroidales and class Bacteroidia are associated with an increased risk of pancreatitis. The genera Coprococcus and Eubacterium fissicatena group also exhibit a positive effect on the development of pancreatitis, while the genera Prevotella, Ruminiclostridium, and Ruminococcaceae act as protective factors against pancreatitis. In contrast, acute pancreatitis was positively correlated with phylum Proteobacteria and genus Lachnospiraceae and negatively correlated with genus Holdemania.

CONCLUSIONS

The bidirectional relationship between gut microbiota and acute pancreatitis suggests a critical role for host-microbiota crosstalk in the development of the disease. Targeted modulation of specific gut microbiota enables the prevention and treatment of acute pancreatitis.

Causal Association between Circulating Metabolites and Dementia: A Mendelian Randomization Study

The causal association of circulating metabolites with dementia remains uncertain. We assessed the causal association of circulating metabolites with dementia utilizing Mendelian randomization (MR) methods. We performed univariable MR analysis to evaluate the associations of 486 metabolites with dementia, Alzheimer's disease (AD), and vascular dementia (VaD) risk. For secondary validation, we replicated the analyses using an additional dataset with 123 metabolites. We observed 118 metabolites relevant to the risk of dementia, 59 of which were lipids, supporting the crucial role of lipids in dementia pathogenesis. After Bonferroni adjustment, we identified nine traits of HDL particles as potential causal mediators of dementia. Regarding dementia subtypes, protective effects were observed for epiandrosterone sulfate on AD (OR = 0.60, 95% CI: 0.48-0.75) and glycoproteins on VaD (OR = 0.89, 95% CI: 0.83-0.95). Bayesian model averaging MR (MR-BMA) analysis was further conducted to prioritize the predominant metabolites for dementia risk, which highlighted the mean diameter of HDL particles and the concentration of very large HDL particles as the predominant protective factors against dementia. Moreover, pathway analysis identified 17 significant and 2 shared metabolic pathways. These findings provide support for the identification of promising predictive biomarkers and therapeutic targets for dementia.

Causal relationship between gut microbiota and diabetic complications: a two-sample Mendelian randomization study

BACKGROUND

Imbalances in gut microbiota (GM) have been proposed as a potential contributing factor to diabetic complications; however, the causal relationship remains incompletely understood.

METHODS

Summary statistics were obtained from genome-wide association studies (GWAS) of 196 gut microbial taxa, including 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera. These data were then analyzed using mediation Mendelian randomization (MR) analyses to explore the potential mediating effect of diabetes complications risk factors on the relationship between gut microbiota and specific diabetic complications such as diabetic kidney disease (DKD), ketoacidosis, and diabetic retinopathy (DR).

RESULTS

In our Mendelian analysis, we observed negative associations between Bifidobacterial order and Actinomycete phylum with DKD in type 1 diabetes (T1D) as well as early DKD in T1D. Conversely, these taxa showed positive associations with ketoacidosis in type 2 diabetes (T2D). In reverse Mendelian analysis, we found that DR in both T1D and T2D as well as ketoacidosis in T2D affected the abundance of Eubacterium fissicaten genus and LachnospiraceaeUCG010 family within the gut microbiota.

CONCLUSIONS

Our findings provide compelling evidence for causal relationships between specific GM taxa and various diabetes complications. These insights contribute valuable knowledge for developing treatments targeting diabetes-related complications.

A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa

BACKGROUND

Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification.

METHODS

With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted.

RESULTS

Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences.

CONCLUSION

By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.

Cardiometabolic traits mediating the effect of education on the risk of DKD and CKD: a Mendelian randomization study

Background

Both diabetic kidney disease (DKD) and chronic kidney disease (CKD) are more prevalent among individuals with lower levels of education in observational studies. To quantify the mediation effect of recognized cardiometabolic traits, we obtain causal estimates between education and DKD as well as CKD.

Materials and methods

We assessed the causal effect of education on DKD and CKD, separately estimated the causal effect of 26 cardiometabolic traits on DKD and CKD, and finally calculated the mediating effects and mediating proportions of each using two-step, two-sample multivariable Mendelian randomization (MVMR). Furthermore, the genetic association between exposure, mediators, and outcomes was investigated using linkage disequilibrium score (LDSC) regression analysis. Expression quantitative trait loci (eQTL) were retrieved from the Genotype-Tissue Expression Project (GTEx) v8 to serve as genetic instrumental variables. Transcriptome-wide association studies (TWAS), Bayesian colocalization analysis, and Summary-data-based Mendelian Randomization (SMR) analysis were performed to explore underlying susceptibility genes between education, mediators, and kidney diseases.

Results

Higher education with a genetically predicted 1-SD (4.2 years) was linked to a 48.64% decreased risk of DKD and a 29.08% decreased risk of CKD. After extensive evaluation of 26 cardiometabolic traits, 7 and 6 causal mediators were identified as mediating the effects of education on DKD and CKD, respectively. The largest mediating factor between education and DKD was BMI, which was followed by WHR, T2D, fasting insulin, SBP, fasting glucose, and DBP. In contrast, candidate mediators in the education-to-CKD pathway included BMI, followed by cigarettes smoked per day, WHR, SBP, T2D, and DBP. MR analysis revealed that TP53INP1 was found to be a shared susceptibility gene for cardiometabolic traits and DKD, while L3MBTL3 was found to be a shared susceptibility gene for cardiometabolic traits and CKD.

Conclusion

Our findings provide solid evidence that education has a causally protective effect on the development of DKD and CKD. We additionally reveal significant directions for intervention on cardiometabolic traits that mitigate the negative effects of educational inequities on the onset of DKD and CKD. Our work demonstrates a shared genetic basis between education, cardiometabolic traits, and kidney diseases. Future research aiming at lowering kidney risk may benefit from these findings.

Genetic susceptibility association between viral infection and colorectal cancer risk: a two-sample Mendelian randomization analysis

BACKGROUND

The genetic susceptibility association between viral infection and the risk of colorectal cancer (CRC) has not been established.

METHODS

We conducted two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) data. In addition to traditional MR methods, we employed several other approaches, including cML, ConMix, MR-RAPS, and dIVW, to comprehensively assess causal effects. Sensitivity analyses were also performed to ensure the robustness of the results.

RESULTS

After sensitivity analysis, presence of SNPs linked to increased susceptibility to cold sores infection was found to decrease the risk of CRC (OR: 0.73, 95% CI: 0.57-0.93, P = 0.01). In subgroup analysis, presence of SNPs linked to increased susceptibility to viral hepatitis (OR: 0.89, 95% CI: 0.81-0.98, P = 0.02) and infectious mononucleosis (OR: 0.91, 95% CI: 0.84-0.98, P = 0.02) were associated with a decreased risk of colon cancer, while measles virus (OR: 1.41, 95% CI: 1.07-1.85, P = 0.01) was associated with an increased risk of colon cancer. Presence of SNPs linked to increased susceptibility to herpes zoster (OR: 1.26, 95% CI: 1.05-1.52, P = 0.01) was associated with an increased risk of rectal cancer, while infectious mononucleosis (OR: 0.809, 95% CI: 0.80-0.98, P = 0.02) was associated with a decreased risk.

CONCLUSION

The study provides the first evidence of the genetic susceptibility associations between different viral infections and CRC, enhancing our understanding of the etiology of CRC.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Exploring pleiotropy in Mendelian randomisation analyses: What are genetic variants associated with 'cigarette smoking initiation' really capturing?

Genetic variants used as instruments for exposures in Mendelian randomisation (MR) analyses may have horizontal pleiotropic effects (i.e., influence outcomes via pathways other than through the exposure), which can undermine the validity of results. We examined the extent of this using smoking behaviours as an example. We first ran a phenome-wide association study in UK Biobank, using a smoking initiation genetic instrument. From the most strongly associated phenotypes, we selected those we considered could either plausibly or not plausibly be caused by smoking. We examined associations between genetic instruments for smoking initiation, smoking heaviness and lifetime smoking and these phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). We conducted negative control analyses among never smokers, including children. We found evidence that smoking-related genetic instruments were associated with phenotypes not plausibly caused by smoking in UK Biobank and (to a lesser extent) ALSPAC. We observed associations with phenotypes among never smokers. Our results demonstrate that smoking-related genetic risk scores are associated with unexpected phenotypes that are less plausibly downstream of smoking. This may reflect horizontal pleiotropy in these genetic risk scores, and we would encourage researchers to exercise caution this when using these and genetic risk scores for other complex behavioural exposures. We outline approaches that could be taken to consider this and overcome issues caused by potential horizontal pleiotropy, for example, in genetically informed causal inference analyses (e.g., MR) it is important to consider negative control outcomes and triangulation approaches, to avoid arriving at incorrect conclusions.

Association between cathepsins and skin cancers: A bidirectional two-sample Mendelian randomization study

BACKGROUND

Several cathepsins have been identified as being involved in the development of cancer. Nevertheless, the connection between cathepsins and skin cancers remained highly elusive.

METHODS

A bidirectional Mendelian randomization (MR) analysis was performed to investigate the causal association between cathepsins and skin malignancies. The genome-wide association studies (GWAS) data for cathepsins, malignant melanoma (MM), and basal cell carcinoma (BCC) were obtained from European research. The primary method employed was inverse variance weighted. In addition, MR-Egger, weighted median, weighted mode, and simple mode were also executed. Sensitivity analysis was performed using Cochran's Q test, MR-Egger, and MR-PRESSO.

RESULTS

From univariable MR (UVMR), cathepsin H, and S were determined to have a causal relationship with BCC. Additionally, cathepsin H was identified as associated with MM. Multivariable MR (MVMR) showed that after correcting for risk factors of skin carcinoma, cathepsin H was detected to be protective against BCC, whereas cathepsin S has been observed as a risk factor for BCC. No substantial pleiotropy and heterogeneity were identified in the sensitivity analysis.

CONCLUSION

This study was the first to establish a direct link between cathepsins and skin malignancies. Cathepsin H and S have the potential to serve as new biomarkers for BCC, offering valuable assistance in the prompt identification, treatment, and prevention of the disease. Nevertheless, additional clinical trials are required to validate our findings.

Statins inhibit paclitaxel-induced PD-L1 expression and increase CD8+ T cytotoxicity for better prognosis in breast cancer

BACKGROUND

In recent years, the widespread use of lipid-lowering drugs, especially statins, has attracted people's attention. Statin use may be potentially associated with a reduced risk of breast cancer.

OBJECTIVE

To explore the relationship between statin use and cancer risk. And further explore the potential role of statins in the adjuvant treatment of breast cancer.

METHODS

Data for the Mendelian randomization portion of the study were obtained from genome-wide association studies of common cancers in the UK Biobank and FinnGen studies and from the Global Lipid Genetics Consortium's low density lipoprotein (LDL). In addition, the impacts of statins and chemotherapy drugs on breast cancer were examined using both in vitro and in vivo models, with particular attention to the expression levels of the immune checkpoint protein PD-L1 and its potential to suppress tumor growth.

RESULTS

Data from about 3.8 million cancer patients and ~1.3 million LDL-measuring individuals were analyzed. Genetically proxied HMGCR inhibition (statins) was associated with breast cancer risk reduction (P=0.0005). In vitro experiments showed that lovastatin significantly inhibited paclitaxel-induced PD-L1 expression and assisted paclitaxel in suppressing tumor cell growth. Furthermore, the combination therapy involving lovastatin and paclitaxel amplified CD8+ T-cell infiltration, bolstering their tumor-killing capacity and enhancing in vivo efficacy.

CONCLUSION

The utilization of statins is correlated with improved prognoses for breast cancer patients and may play a role in facilitating the transition from cold to hot tumors. Combination therapy with lovastatin and paclitaxel enhances CD8+ T-cell activity and leads to better prognostic characteristics.

Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease

BACKGROUND

Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD).

METHODS

To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization. Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants.

FINDINGS

We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue.

INTERPRETATION

Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis.

FUNDING

NIH grants R01HG010505, R01DK132775, and R01HL170604; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 802825), Academy of Finland (Grants Nos. 333021), the Finnish Foundation for Cardiovascular Research the Sigrid Jusélius Foundation and the Jane and Aatos Erkko Foundation; American Association for the Study of Liver Diseases (AASLD) Advanced Transplant Hepatology award and NIH/NIDDK (P30DK41301) Pilot and Feasibility award; NIH/NIEHS F32 award (F32ES034668); Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2021), the Academy of Finland grant (Contract no. 138006); Academy of Finland (Grant Nos 335443, 314383, 272376 and 266286), Sigrid Jusélius Foundation, Finnish Medical Foundation, Finnish Diabetes Research Foundation, Novo Nordisk Foundation (#NNF20OC0060547, NNF17OC0027232, NNF10OC1013354) and Government Research Funds to Helsinki University Hospital; Orion Research Foundation, Maud Kuistila Foundation, Finish Medical Foundation, and University of Helsinki.

Metabolites mediate the causal associations between gut microbiota and NAFLD: a Mendelian randomization study

BACKGROUND

Although gut microbiota and serum metabolite composition have been observed to be altered in patients with non-alcoholic fatty liver disease (NAFLD), previous observational studies have demonstrated inconsistent results. As this may be influenced by factors such as confounders and reverse causality, we used Mendelian randomization to clarify the causal effect of gut microbiota and blood metabolites on NAFLD.

METHODS

In this research, we performed a two-step Mendelian randomization analysis by utilizing genome-wide association study (GWAS) data obtained from MiBioGen and UK Biobank. To mitigate potential errors, we employed False Discovery Rate (FDR) correction and linkage unbalanced regression (LDSC) analysis. Sensitivity analyses including cML-MA and bidirectional Mendelian randomization were performed to ensure the robustness of the results.

RESULTS

In this study, a total of nine gut microbiota and seven metabolites were found to be significantly associated with NAFLD. MR analysis of the above findings revealed a causal relationship between Ruminococcus2 and cysteine-glutathione disulfide (OR = 1.17, 95%CI = 1.006-1.369, P = 0.041), as well as 3-indoleglyoxylic acid (OR = 1.18, 95%CI = 1.011-1.370, P = 0.036). For each incremental standard deviation in Ruminococcus2 abundance, there was a corresponding 26% reduction in NAFLD risk (OR = 0.74, 95%CI = 0.61-0.89, P = 0.0012), accompanied by a 17% increase in cysteine-glutathione disulfide levels (OR = 1.17, 95%CI = 1.01-1.37, P = 0.041) and an 18% increase in 3-indoleglyoxylic acid levels (OR = 1.18, 95%CI = 0.81-1.00, P = 0.036). The proportion mediated by cysteine-glutathione disulfide is 11.2%, while the proportion mediated by 3-indoleglyoxylic acid is 7.5%.

CONCLUSION

Our study suggests that increased abundance of specific gut microbiota may reduce the risk of developing NAFLD, and this relationship could potentially be mediated through blood metabolites.

Pain, obesity, adenosine salvage disruption, and smoking behavior mediate the effect of gut microbiota on sleep disorders: results from network Mendelian randomization and 16S rDNA sequencing

Objectives

The objective of this study is to investigate the indirect causalities between gut microbiota and sleep disorders.

Methods

In stage 1, we utilized 196 gut microbiota as the exposure factor and conducted a two-sample univariable Mendelian randomization (MR) analysis on five sleep disorders: insomnia, excessive daytime sleepiness (EDS), sleep-wake rhythm disorders (SWRD), obstructive sleep apnea (OSA), and isolated REM sleep behavior disorder (iRBD). In stage 2, we validated the MR findings by comparing fecal microbiota abundance between patients and healthy controls through 16S rDNA sequencing. In stage 3, we explored the indirect pathways by which the microbiota affects sleep, using 205 gut microbiota metabolic pathways and 9 common risk factors for sleep disorders as candidate mediators in a network MR analysis.

Results

In stage 1, the univariable MR analysis identified 14 microbiota potentially influencing five different sleep disorders. In stage 2, the results from our observational study validated four of these associations. In stage 3, the network MR analysis revealed that the Negativicutes class and Selenomonadales order might worsen insomnia by increasing pain [mediation: 12.43% (95% CI: 0.47, 24.39%)]. Oxalobacter could raise EDS by disrupting adenosine reuptake [25.39% (1.84, 48.95%)]. Allisonella may elevate OSA risk via obesity promotion [36.88% (17.23, 56.54%)], while the Eubacterium xylanophilum group may lower OSA risk by decreasing smoking behavior [7.70% (0.66, 14.74%)].

Conclusion

Triangulation of evidence from the MR and observational study revealed indirect causal relationships between the microbiota and sleep disorders, offering fresh perspectives on how gut microbiota modulate sleep.

Rheumatoid arthritis and adverse pregnancy outcomes: a bidirectional two-sample mendelian randomization study

BACKGROUND

There is growing evidence of bidirectional associations between rheumatoid arthritis and adverse pregnancy outcomes (APOs) in observational studies, but little is known about the causal direction of these associations. Therefore, we explored the potential causal relationships between rheumatoid arthritis and APOs using a bidirectional two-sample Mendelian randomization (MR) in European and Asian populations.

METHODS

We conducted a bidirectional two-sample Mendelian randomization analysis using available summary statistics from released genome-wide association studies. Summary statistics for instrument-outcome associations were retrieved from two separate databases for rheumatoid arthritis and adverse pregnancy outcomes, respectively. The inverse-variance weighted method was used as the primary MR analysis, and cML-MA-BIC was used as the supplementary analysis. MR-Egger, MR pleiotropy residual sum and outlier (MR-PRESSO), and Cochran Q statistic method were implemented as sensitivity analyses approach to ensure the robustness of the results.

RESULTS

Our study showed that a higher risk of rheumatoid arthritis in the European population was associated with gestational hypertension (OR: 1.04, 95%CI: 1.02-1.06), pre-eclampsia (OR: 1.06, 95%CI: 1.01-1.11), fetal growth restriction (OR: 1.08, 95%CI: 1.04-1.12), preterm delivery (OR:1.04, 95%CI: 1.01-1.07). Furthermore, we found no evidence that APOs had causal effects on rheumatoid arthritis in the reverse MR analysis. No association between rheumatoid arthritis and APOs was found in East Asian population. There was no heterogeneity or horizontal pleiotropy.

CONCLUSIONS

This MR analysis provides the positive causal association from rheumatoid arthritis to gestational hypertension, pre-eclampsia, fetal growth restriction and preterm delivery genetically. It highlights the importance of more intensive prenatal care and early intervention among pregnant women with rheumatoid arthritis to prevent potential adverse obstetric outcomes.

Genetically predicted small dense low-density lipoprotein cholesterol and ischemic stroke subtype: multivariable Mendelian randomization study

Purpose

Small dense low-density lipoprotein cholesterol (S-LDL-C) has been suggested as a particularly atherogenic factor for ischemic stroke (IS) in observational studies, but the causality regarding the etiological subtype remains unclear. This study aims to explore the causal effects of small dense low-density lipoprotein cholesterol (S-LDL-C), medium (M-LDL-C) and large (L-LDL-C) subfractions on the lifetime risk of ischemic stroke (IS) and main subtypes using two-sample Mendelian randomization (TSMR) design.

Methods

We identified genetic instruments for S-LDL-C, M-LDL-C and L-LDL-C from a genome-wide association study of 115 082 UK Biobank participants. Summary-level data for genetic association of any ischemic stroke (AIS), large artery stroke (LAS), small vessel stroke (SVS) and cardioembolic stroke (CES) were obtained from MEGASTROKE consortium. Accounting for the pleiotropic effects of triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), we conducted multivariable TSMR analysis.

Results

In univariable TSMR, we found a causal association between genetically predicted S-LDL-C and LAS (IVW-FE: odds ratio (OR) = 1.481, 95% confidence interval (CI): 1.117-1.963, P = 0.006, q = 0.076) but not AIS, SVS or CES. No causal effects were observed for M-LDL-C or L-LDL-C in terms of AIS and IS subtype. In multivariable analysis, the causal association between S-LDL-C and LAS remained significant (IVE-MRE: OR = 1.329, 95% CI: 1.106-1.597, P = 0.002).

Conclusions

Findings supported a causal association between S-LDL-C and LAS. Further studies are warranted to elucidate the underlying mechanism and clinical benefit of targeting S-LDL-C.

Genetically Predicted Vascular Proteins and Risk of Intracranial Aneurysms: A Mendelian Randomization Study

The relationship between vascular proteins (VPs) and intracranial aneurysms (IAs) has not been fully elucidated. We used Mendelian randomization (MR) analysis to explore the effect of VPs on IAs. Dataset of aneurysmal subarachnoid hemorrhage (aSAH) [5140 cases and 71,934 controls] and unruptured intracranial aneurysm (uIA) [2070 cases and 71,934 controls] were obtained from individuals of European ancestry. Univariate MR was used to explore the associations between 90 VPs and IAs. Then, we performed multivariate MR (MVMR) to further investigate the identified VP-to-IA estimates. Two-sample MR showed that TNFSF14 was inversely associated with aSAH (odds ratio [OR] = 0.831, 95% CI: 0.713-0.969, p = 0.018). IL-16 (OR = 1.218, 95% CI: 1.032-1.438, p = 0.020) and AgRP (OR = 1.394, 95% CI: 1.048-1.855, p = 0.023) were positively associated with aSAH. HBEGF (OR = 0.642, 95% CI: 0.461-0.894, p = 0.009), MCP-1 (OR = 1.537, 95% CI: 1.007-2.344, p = 0.046), and CX3CL1 (OR = 0.762, 95% CI: 0.581-0.999, 0.049 < p < 0.050) were associated with uIA risk. The MVMR showed that the TNFSF14-to-aSAH estimate remained statistically significant after adjustment for past tobacco smoking, alcohol consumption, systolic blood pressure and body mass index. Our study indicated that low serum TNFSF14 levels might be a potential risk factor for IA rupture. Five VPs (HBEGF, MCP-1, IL-6, CX3CL1, and AgRP) are associated with the risk of IAs (both uIA and aSAH).

Exploring the Relationship Between Sporadic Creutzfeldt-Jakob Disease and Gut Microbiota Through a Mendelian Randomization Study

Observational studies have shown gut microbiota changes in sporadic Creutzfeldt-Jakob disease patients, but the causal relationship remains unknown. We aimed to determine any causal links between gut microbiota and this prion disease. Using Mendelian randomization analysis, we examined the causal relationship between gut microbiota composition and sporadic Creutzfeldt-Jakob disease. Data on gut microbiota (N = 18,340) and disease cases (5208) were obtained. Various analysis methods were used, including inverse variance weighted, Mendelian randomization-Egger, weighted median, simple mode, and weighted mode. In addition, MR-PRESSO was used to evaluate horizontal pleiotropy and detect outliers. Pleiotropy and heterogeneity were assessed, and reverse analysis was conducted. Negative associations were found between sporadic Creutzfeldt-Jakob disease and family Defluviitaleaceae, family Ruminococcaceae, genus Butyricicoccus, genus Desulfovibrio, and genus Eubacterium nodatum. Genus Lachnospiraceae UCG010 showed a positive correlation. Reverse analysis indicated genetic associations between the disease and decreased levels of family Peptococcaceae, genus Faecalibacterium, and genus Phascolarctobacterium, as well as increased levels of genus Butyrivibrio. No pleiotropy, heterogeneity, outliers, or weak instrument bias were observed. This study revealed bidirectional causal effects between specific gut microbiota components and sporadic Creutzfeldt-Jakob disease. Certain components demonstrated inhibitory effects on disease pathogenesis, while others were positively associated with the disease. Modulating gut microbiota may provide new insights into prion disease therapies. Further research is needed to clarify mechanisms and explore treatments for sporadic Creutzfeldt-Jakob disease.

Causal associations between both psoriasis and psoriatic arthritis and multiple autoimmune diseases: a bidirectional two-sample Mendelian randomization study

Background

Numerous observational studies have identified associations between both psoriasis (PsO) and psoriatic arthritis (PsA), and autoimmune diseases (AIDs); however, the causality of these associations remains undetermined.

Methods

We conducted a bidirectional two-sample Mendelian Randomization study to identify causal associations and directions between both PsO and PsA and AIDs, such as systemic lupus erythematosus (SLE), Crohn's disease (CD), ulcerative colitis (UC), multiple sclerosis (MS), uveitis, bullous pemphigoid (BP), Hashimoto's thyroiditis (HT), rheumatoid arthritis (RA), vitiligo, and ankylosing spondylitis (AS). The causal inferences were drawn by integrating results from four regression models: Inverse Variance Weighting (IVW), MR-Egger, Weighted Median, and Maximum Likelihood. Furthermore, we performed sensitivity analyses to confirm the reliability of our findings.

Results

The results showed that CD [IVW odds ratio (ORIVW), 1.11; 95% confidence interval (CI), 1.06-1.17; P = 8.40E-06], vitiligo (ORIVW, 1.16; 95% CI, 1.05-1.28; P = 2.45E-03) were risk factors for PsO, while BP may reduce the incidence of PsO (ORIVW, 0.91; 95% CI, 0.87-0.96; P = 1.26E-04). CD (ORIVW, 1.07; 95% CI, 1.02-1.12; P = 0.01), HT (ORIVW, 1.23; 95% CI, 1.08-1.40; P = 1.43E-03), RA (ORIVW, 1.11; 95% CI, 1.02-1.21, P = 2.05E-02), AS (ORIVW, 2.18; 95% CI, 1.46-3.27; P = 1.55E-04), SLE (ORIVW, 1.04; 95% CI, 1.01-1.08; P = 1.07E-02) and vitiligo (ORIVW, 1.27; 95% CI, 1.14-1.42; P = 2.67E-05) were risk factors for PsA. Sensitivity analyses had validated the reliability of the results.

Conclusions

Our study provides evidence for potential causal relationships between certain AIDs and both PsO and PsA. Specifically, CD and vitiligo may increase the risk of developing PsO, while CD, HT, SLE, RA, AS, and vitiligo may elevate the risk for PsA. Additionally, it is crucial to closely monitor the condition of PsO patients with specific AIDs, as they have a higher likelihood of developing PsA than those without AIDs. Moving forward, greater attention should be paid to PsA and further exploration of other PsO subtypes is warranted.

[Causal relationship between gut microbiota and pigmented villonodular synovitis: a Mendelian randomization analysis]

OBJECTIVE

To investigate the causal relationship between gut microbiota and pigmented villonodular synovitis using Mendelian randomization analysis.

METHODS

We conducted a two-sample Mendelian randomization analysis to investigate the causal relationship between 211 gut microbiome taxa and pigmented villonodular synovitis based on GWAS summary data, with inverse variance weighted (IVW) analysis as the primary result and the other methods as supplementary analyses. The reliability of the results was tested using Cochran's Q test, MR-Egger regression, MR-PRESSO method and conditional Mendelian randomization analysis (cML-MA).

RESULTS

The increased abundance of Barnesiella (OR=3.12, 95% CI: 1.15-8.41, P=0.025) and Rumatococcaceae UCG010 (OR=4.03, 95% CI: 1.19-13.68, P=0.025) may increase the risk of pigmented villous nodular synovitis, and elevated abundance of Lachnospiraceae (OR=0.33, 95% CI: 0.12-0.91, P=0.032), Alistipes (OR=0.16, 95% CI: 0.05-0.53, P=0.003), Blautia (OR=0.20, 95% CI: 0.06-0.61, P=0.005), and Lachnospiraceae FCS020 group (OR=0.38, 95% CI: 0.15-0.94, P=0.036) and Ruminococcaceae UCG014 (OR=0.36, 95% CI: 0.14-0.94, P=0.037) were all associated with a reduced risk of pigmented villonodular synovitis, which were supported by the results of sensitivity analyses. Reverse Mendelian randomization analysis did not reveal any inverse causal association.

CONCLUSION

Increased abundance of specific intestinal microorganisms is associated with increased or decreased risks of developing hyperpigmented villonodular synovitis, and gut microbiota plays an important role in the pathogenesis of this disease.

The associations between dysregulation of human blood metabolites and lung cancer risk: evidence from genetic data

BACKGROUND

Metabolic dysregulation is recognized as a significant hallmark of cancer progression. Although numerous studies have linked specific metabolic pathways to cancer incidence, the causal relationship between blood metabolites and lung cancer risk remains unclear.

METHODS

Genomic data from 29,266 lung cancer patients and 56,450 control individuals from the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium (TRICL-ILCCO) were utilized, and findings were replicated using additional data from the FinnGen consortium. The analysis focused on the associations between 486 blood metabolites and the susceptibility to overall lung cancer and its three major clinical subtypes. Various Mendelian randomization methods, including inverse-variance weighting, weighted median estimation, and MR-Egger regression, were employed to ensure the robustness of our findings.

RESULTS

A total of 19 blood metabolites were identified with significant associations with lung cancer risk. Specifically, oleate (OR per SD = 2.56, 95% CI: 1.51 to 4.36), 1-arachidonoylglyceropholine (OR = 1.79, 95% CI: 1.22 to 2.65), and arachidonate (OR = 1.67, 95% CI: 1.16 to 2.40) were associated with a higher risk of lung cancer. Conversely, 1-linoleoylglycerophosphoethanolamine (OR = 0.57, 95% CI: 0.40 to 0.82), ADpSGEGDFXAEGGGVR, a fibrinogen cleavage peptide (OR = 0.60, 95% CI: 0.47 to 0.77), and isovalerylcarnitine (OR = 0.62, 95% CI: 0.49 to 0.78) were associated with a lower risk of lung cancer. Notably, isoleucine (OR = 9.64, 95% CI: 2.55 to 36.38) was associated with a significantly higher risk of lung squamous cell cancer, while acetyl phosphate (OR = 0.11, 95% CI: 0.01 to 0.89) was associated with a significantly lower risk of small cell lung cancer.

CONCLUSION

This study reveals the complex relationships between specific blood metabolites and lung cancer risk, highlighting their potential as biomarkers for lung cancer prevention, screening, and treatment. The findings not only deepen our understanding of the metabolic mechanisms of lung cancer but also provide new insights for future treatment strategies.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.