Causal relationships between height, screen time, physical activity, sleep and myopia: univariable and multivariable Mendelian randomization.

Background: This study aims to investigate the independent causal relation between height, screen time, physical activity, sleep and myopia. Methods: Instrumental variables (IVs) for exposures and outcome were obtained from the largest publicly available genome-wide association studies (GWAS) databases. First, we performed a bidirectional univariate MR analysis using primarily the inverse variance weighted method (IVW) with height, screen time, physical activity and sleep as the exposure and myopia as the outcome to investigate the causal relationship between exposures and myopia. Sensitivity analysis was used to demonstrate its robustness. Then the multivariable MR (MVMR) and MR-based mediation approach was further used to estimate the mediating effect of potential confounders (education and time outdoors) on causality. Results: The results of univariate MR analysis showed that taller height (OR = 1.009, 95% CI = 1.005-1.012, p = 3.71 × 10-7), longer time on computer (OR = 1.048, 95% CI = 1.029-1.047, p = 3.87 × 10-7) and less moderate physical activity (OR = 0.976, 95% CI = 0.96-0.991 p = 2.37 × 10-3) had a total effect on the increased risk of developing myopia. Meanwhile our results did not have sufficient evidence to support the causal relationship between chronotype (p = 0.637), sleep duration (p = 0.952) and myopia. After adjusting for education, only taller height remains an independent risk factor for myopia. After adjusting for education, the causal relationship between height, screen and myopia still had statistical significance. A reverse causal relationship was not found in our study. Most of the sensitivity analyses showed consistent results with those of the IVW method. Conclusion: Our MR study revealed that genetically predicted taller height, longer time on computer, less moderate physical activity increased the risk of myopia. After full adjustment for confounders, only height remained independently associated with myopia. As a complement to observational studies, the results of our analysis provide strong evidence for the improvement of myopia risk factors and provide a theoretical basis for future measures to prevent and control myopia in adolescents.

Bidirectional causality between the levels of blood lipids and endometriosis: a two-sample mendelian randomization study.

BACKGROUND: Observational studies have found a correlation between the levels of blood lipids and the development and progression of endometriosis (EM). However, the causality and direction of this correlation is unclear. This study aimed to examine the bidirectional connection between lipid profiles and the risk of EM using publicly available genome-wide association study (GWAS) summary statistics. METHODS: Eligible exposure variables such as levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were selected using a two-sample Mendelian randomization (MR) analysis method following a series of quality control procedures. Data on EM were obtained from the publicly available Finnish database of European patients. Inverse variance weighted (IVW), MR Egger, weighted median, and weighted mode methods were used to analyze the causal relationship between lipid exposure and EM, exclude confounders, perform sensitivity analyses, and assess the stability of the results. Reverse MR analyses were performed with EM as exposure and lipid results as study outcomes. RESULTS: IVW analysis results identified HDL as a protective factor for EM, while TG was shown to be a risk factor for EM. Subgroup analyses based on the site of the EM lesion identified HDL as a protective factor for EM of the uterus, while TG was identified a risk factor for the EM of the fallopian tube, ovary, and pelvic peritoneum. Reverse analysis did not reveal any effect of EM on the levels of lipids. CONCLUSION: Blood lipids, such as HDL and TG, may play an important role in the development and progression of EM. However, EM does not lead to dyslipidemia.

The causal effect of atopic dermatitis on lung cancer: A Mendelian randomization study.

BACKGROUND: Growing evidence has shown that atopic dermatitis (AD) may decrease lung cancer (LC) risk. However, the causality between the two diseases is inconsistent and controversial. Therefore, we explored the causal relationship between AD and different histological subtypes of LC by using the Mendelian randomization (MR) method. MATERIALS AND METHODS: We conducted the MR study based on summary statistics from the genome-wide association studies (GWAS) of AD (10,788 cases and 30,047 controls) and LC (29,266 cases and 56,450 controls). Instrumental variables (IVs) were obtained after removing SNPs associated with potential confounders. We employed inverse-variance weighted (IVW), MR-Egger, and weighted median methods to pool estimates, and performed a comprehensive sensitivity analysis. RESULTS: The results of the IVW method suggested that AD may decrease the risk of developing lung adenocarcinoma (LUAD) (OR = 0.91, 95% CI: 0.85-0.97, P = 0.007). Moreover, no causality was identified between AD and overall LC (OR = 0.96, 95% CI: 0.91-1.01, P = 0.101), lung squamous cell carcinoma (LUSC) (OR = 1.04, 95% CI: 0.96-1.036, P = 0.324), and small cell lung carcinoma (SCLC) (OR = 0.95, 95% CI: 0.82-1.10, P = 0.512). A comprehensive sensitivity test showed the robustness of our results. CONCLUSION: The present study indicates that AD may decrease the risk of LUAD in the European population, which needs additional investigations to identify the potential molecular mechanisms.

The causality between leisure sedentary behaviors, physical activity and obstructive sleep apnea: a bidirectional Mendelian randomization study.

Background: Previous observational studies have shown a correlation between leisure sedentary behaviors (LSB) and physical activity (PA) with the incidence of obstructive sleep apnea (OSA). However, the causal associations remain unknown. Therefore, our study used bidirectional two-sample Mendelian randomization (MR) to identify potential causal relationships between LSB/PA and OSA. Methods: We sourced genetic variation data for LSB and PA from the UK Biobank, while data on OSA were collected from the FinnGen study. The primary analysis method employed was the inverse variance weighted (IVW) approach, complemented by the weighted median and MR-Egger methods. For sensitivity analyses, we conducted Cochran's Q test, the MR-Egger intercept test, the MR-PRESSO global test, and the leave-one-out analysis. Results: IVW analyses showed that genetically predicted leisure television watching (odds ratio [OR] = 1.38, 95% confidence interval [CI] = 1.09-1.75, p = 0.007) and computer use (OR = 1.48, 95% CI = 1.15-1.92, p = 0.002) significantly increased the risk of OSA. Conversely, self-reported vigorous physical activity (VPA) (OR = 0.33, 95% CI = 0.11-0.98, p = 0.046) may reduce the risk of OSA. No causal effects on OSA risk were observed for driving or self-reported moderate-to-vigorous physical activity. Furthermore, the reverse MR analysis indicated no significant causal relationship between OSA and any LSB/PA phenotype. Sensitivity tests showed no significant heterogeneity or horizontal pleiotropy. Conclusion: This study suggests that leisurely television watching and computer use are risk factors for OSA, while VPA may be a protective factor. Additionally, OSA does not affect PA or LSB levels. We recommend reducing sedentary activities, particularly television watching and computer use, and prioritizing VPA to reduce the risk of OSA. Further research in diverse populations and settings is needed to validate these findings.

Reverse causation bias: A simulation study comparing first- and second-line treatments with an overlap of symptoms between treatment indication and studied outcome.

BACKGROUND: Reverse causation is a challenge in many drug-cancer associations, where the cancer symptoms are potentially mistaken for drug indication symptoms. However, tools to assess the magnitude of this type of bias are currently lacking. We used a simulation-based approach to investigate the impact of reverse causation on the association between the use of topical tacrolimus and cutaneous T-cell lymphoma (CTCL) in a multinational, population-based study using topical corticosteroids (TCS) as comparator. METHODS: We used a multistate model to simulate patients' use over time of a first- (TCS) and second-line treatment (topical tacrolimus), onset of atopic dermatitis (indication for drugs) and CTCL (the studied outcome). We simulated different scenarios to mimic real-life use of the two treatments. In all scenarios, it was assumed that there was no causal effect of the first- or second-line treatment on the occurrence of CTCL. Simulated data were analysed using Cox proportional hazards models. RESULTS: The simulated hazard ratios (HRs) of CTCL for patients treated with tacrolimus vs. TCS were consistently above 1 in all 9 settings in the main scenario. In our main analysis, we observed a median HR of 3.09 with 95% of the observed values between 2.11 and 4.69. CONCLUSIONS: We found substantial reverse causation bias in the simulated CTCL risk estimates for patients treated with tacrolimus vs. TCS. Reverse causation bias may result in a false positive association between the second-line treatment and the studied outcome, and this simulation-based framework can be adapted to quantify the potential reverse causation bias.

Mapping complex public health problems with causal loop diagrams.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

A Causality Assessment Framework for COVID-19 Vaccines and Adverse Events at the COVID-19 Vaccine Safety Research Center.

During the coronavirus disease 2019 (COVID-19) pandemic, conclusively evaluating possible associations between COVID-19 vaccines and potential adverse events was of critical importance. The National Academy of Medicine of Korea established the COVID-19 Vaccine Safety Research Center (CoVaSC) with support from the Korea Disease Control and Prevention Agency to investigate the scientific relationship between COVID-19 vaccines and suspected adverse events. Although determining whether the COVID-19 vaccine was responsible for any suspected adverse event necessitated a systematic approach, traditional causal inference theories, such as Hill's criteria, encountered certain limitations and criticisms. To facilitate a systematic and evidence-based evaluation, the United States Institute of Medicine, at the request of the Centers for Disease Control and Prevention, offered a detailed causality assessment framework in 2012, which was updated in the recent report by the National Academies of Sciences, Engineering, and Medicine (NASEM) in 2024. This framework, based on a weight-of-evidence approach, allows the independent evaluation of both epidemiological and mechanistic evidence, culminating in a comprehensive conclusion about causality. Epidemiological evidence derived from population studies is categorized into four levels-high, moderate, limited, or insufficient-while mechanistic evidence, primarily from biological and clinical studies in animals and individuals, is classified as strong, intermediate, weak, or lacking. The committee then synthesizes these two types of evidence to draw a conclusion about the causal relationship, which can be described as "convincingly supports" ("evidence established" in the 2024 NASEM report), "favors acceptance," "favors rejection," or "inadequate to accept or reject." The CoVaSC has established an independent committee to conduct causality assessments using the weight-of-evidence framework, specifically for evaluating the causality of adverse events associated with COVID-19 vaccines. The aim of this study is to provide an overview of the weight-of-evidence framework and to detail the considerations involved in its practical application in the CoVaSC.

The causal relationship between inflammatory bowel diseases and erythema nodosum: a bidirectional two-sample mendelian randomization study.

BACKGROUND: Individuals with inflammatory bowel disease (IBD) exhibit a heightened likelihood of developing erythema nodosum (EN), but the presence of causal link is unknown. The purpose of the present research was to investigate this connection using a bidirectional two-sample Mendelian randomization (MR) analysis. METHODS: Summarized statistics for EN were sourced from the FinnGen consortium of European ancestry. The International Inflammatory Bowel Disease Genetic Consortium (IBDGC) was used to extract summary data for IBD. The inverse variance weighted (IVW) technique was the major method used to determine the causative link between them. RESULTS: The study evaluated the reciprocal causal link between IBD and EN. The IVW technique confirmed a positive causal link between IBD and EN (OR = 1.237, 95% CI: 1.109-1.37, p = 1.43 × 10- 8), as well as a strong causality connection between Crohn's disease (CD) and EN (OR = 1.248, 95% CI: 1.156-1.348, p = 1.00 × 10- 4). Nevertheless, a causal connection between ulcerative colitis (UC) and EN could not be established by the data. The reverse MR research findings indicated that analysis indicated that an increase in EN risks decreased the likelihood of UC (OR = 0.927, 95% CI: 0.861-0.997, p = 0.041), but the causal association of EN to IBD and CD could not be established. CONCLUSION: This investigation confirmed that IBD and CD had a causal connection with EN, whereas UC did not. In addition, EN may decrease the likelihood of UC. Further study must be performed to uncover the underlying pathophysiological mechanisms producing that connection.

On formal limitations of causal ecological networks.

Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.

Sampling weighting strategies in causal mediation analysis.

BACKGROUND: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis. METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation "cross-world" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH). RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions. DISCUSSION: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

Causal effects between gut microbiota and endometriosis: a two-sample Mendelian randomisation study.

BACKGROUND: Previous observational evidence has indicated the potential involvement of the gut microbiota (GM) in the development of endometriosis. However, the causal relationship of the association remains to be investigated. METHOD: Genome-wide association study (GWAS) data of GM was obtained from the MiBioGen consortium, and GWAS for endometriosis data was from the FinnGen consortium. Initially, a two-sample Mendelian randomisation (MR) analysis was performed to identify specific bacteria associated with endometriosis. Inverse variance-weighted (IVW) was used as the main MR analysis to infer causal relationships. The other four popular MR methods including MR-Egger regression, weighted mode, weighted median, and simple mode were used for secondary confirmation. Subsequently, these selected bacteria were employed as exposure to investigate their causal effects on six sub-types of endometriosis. Furthermore, reverse MR analysis was implemented to evaluate the reverse causal effects. Cochran's Q statistics was used to test the heterogeneity of instrumental variables (IVs); MR-Egger regression was used to test horizontal pleiotropy; MR-PRESSO and leave-one-out sensitivity analysis were applied to find significant outliers. RESULT: A total of 1131 single nucleotide polymorphisms (SNPs) were collected as IVs for 196 GM taxa with endometriosis as the outcome. We identified 12 causal relationships between endometriosis and GM taxa including 1 phylum, 3 families, 2 orders, and 6 genera (Rikenellaceae RC9 gut group, Eubacterium ruminantium group, Faecalibacterium, Peptococcus, Clostridium sensu stricto 1, and Ruminococcaceae UCG005). Utilizing the Bonferroni method, we identified phylum Cyanobacteria as the strongest associated GM taxa. Subsequently, 6 significant causal effects were uncovered between the 12 selected specific GM and 6 sub-types of endometriosis. Meanwhile, no reverse causal relationship was found. Further, no horizontal pleiotropy and no significant outliers were detected in the sensitive analysis. CONCLUSIONS: This MR analysis revealed significant causal effects between GM and endometriosis and phylum Cyanobacteria had the strongest association.

The imbalance of gut microbiota (GM) is suggested to be involved in the development of endometriosis while the causal relationship between GM and endometriosis remains undetermined. This two-sample mendelian randomisation analysis firstly demonstrated the potential association between GM and the risk of endometriosis including 6 sub-types. We revealed 12 causal relationships between endometriosis and GM taxa including 1 phylum, 3 families, 2 orders, and 6 genera while Phylum Cyanobacteria was the strongest associated GM taxa by using Bonferroni method. Meanwhile, we identified 6 significant causal effects between 12 selected specific GM and 6 sub-types of endometriosis. Meanwhile, the result from reverse MR analysis showed that there was no causal effect of endometriosis on the identified specific GM taxa. Thus, we revealed the causal relationship between GM and endometriosis. It is necessary to further study its potential mechanism, which may contribute to the prevention and treatment of Endometriosis.

Causal association between particulate matter 2.5 and Alzheimer's disease: a Mendelian randomization study.

Background: Although epidemiological evidence implies a link between exposure to particulate matter (PM) and Alzheimer's disease (AD), establishing causality remains a complex endeavor. In the present study, we used Mendelian randomization (MR) as a robust analytical approach to explore the potential causal relationship between PM exposure and AD risk. We also explored the potential associations between PM exposure and other neurodegenerative diseases. Methods: Drawing on extensive genome-wide association studies related to PM exposure, we identified the instrumental variables linked to individual susceptibility to PM. Using summary statistics from five distinct neurodegenerative diseases, we conducted two-sample MR analyses to gauge the causal impact of PM on the risk of developing these diseases. Sensitivity analyses were undertaken to evaluate the robustness of our findings. Additionally, we executed multivariable MR (MVMR) to validate the significant causal associations identified in the two-sample MR analyses, by adjusting for potential confounding risk factors. Results: Our MR analysis identified a notable association between genetically predicted PM2.5 (PM with a diameter of 2.5 µm or less) exposure and an elevated risk of AD (odds ratio, 2.160; 95% confidence interval, 1.481 to 3.149; p < 0.001). A sensitivity analysis supported the robustness of the observed association, thus alleviating concerns related to pleiotropy. No discernible causal relationship was identified between PM and any other neurodegenerative diseases. MVMR analyses-adjusting for smoking, alcohol use, education, stroke, hearing loss, depression, and hypertension-confirmed a persistent causal relationship between PM2.5 and AD. Sensitivity analyses, including MR-Egger and weighted median analyses, also supported this causal association. Conclusion: The present MR study provides evidence to support a plausible causal connection between PM2.5 exposure and AD. The results emphasize the importance of contemplating air quality interventions as a public health strategy for reducing AD risk.

Mendelian randomization supports causality between COVID-19 and glaucoma.

To determine whether there is a causal relationship between Corona Virus Disease 2019 (COVID-19) and glaucoma, a 2-sample Mendelian Randomization (MR) design was applied with the main analysis method of inverse-variance-weighted. The reliability of the results was checked using the heterogeneity test, pleiotropy test, and leave-one-out method. Four sets of instrumental variables (IVs) were used to investigate the causality between COVID-19 and glaucoma risk according to data from the IEU Genome Wide Association Study (GWAS). The results showed that 2 sets of COVID-19(RELEASE) were significantly associated with the risk of glaucoma [ID: ebi-a-GCST011071, OR (95% CI) = 1.227 (1.076-1.400), P = .002259; ID: ebi-a-GCST011073: OR (95% CI) = 1.164 (1.022-1.327), P = .022450; 2 sets of COVID-19 hospitalizations were significantly associated with the risk of glaucoma (ID: ebi-a-GCST011081, OR (95% CI) = 1.156 (1.033-1.292), P = .011342; ID: ebi-a-GCST011082: OR (95% CI) = 1.097 (1.007-1.196), P = .034908)]. The sensitivity of the results was acceptable (P > .05) for the 3 test methods. In conclusion, this MR analysis provides preliminary evidence of a potential causal relationship between COVID-19 and glaucoma.

Causal effects for neurodegenerative diseases on the risk of myocardial infarction: a two-sample Mendelian randomization study.

Several studies have demonstrated a correlation between neurodegenerative diseases (NDDs) and myocardial infarction (MI), yet the precise causal relationship between these remains elusive. This study aimed to investigate the potential causal associations of genetically predicted Alzheimer's disease (AD), dementia with Lewy bodies (DLB), Parkinson's disease (PD), and multiple sclerosis (MS) with MI using two-sample Mendelian randomization (TSMR). Various methods, including inverse variance weighted (IVW), weighted median (WM), MR-Egger regression, weighted mode, and simple mode, were employed to estimate the effects of genetically predicted NDDs on MI. To validate the analysis, we assessed pleiotropic effects, heterogeneity, and conducted leave-one-out sensitivity analysis. We identified that genetic predisposition to NDDs was suggestively associated with higher odds of MI (OR_IVW=1.07, OR_MR-Egger=1.08, OR_WM=1.07, OR_weighted mode=1.07, OR_simple mode=1.10, all P<0.05). Furthermore, we observed significant associations of genetically predicted DLB with MI (OR_IVW=1.07, OR_MR-Egger=1.11, OR_WM=1.09, OR_weighted mode=1.09, all P<0.05). However, there was no significant causal evidence of genetically predicted PD and MS in MI. Across all MR analyses, no horizontal pleiotropy or statistical heterogeneity was observed (all P>0.05). Additionally, results from MRPRESSO and leave-one-out sensitivity analysis confirmed the robustness of the causal effect estimations for genetically predicted AD, DLB, PD, and MS on MI. This study provides further support for the causal effects of AD on MI and, for the first time, establishes robust causal evidence for the detrimental effect of DLB on the risk of MI. Our findings emphasize the importance of monitoring the cardiovascular function of the elderly experiencing neurodegenerative changes.

Mendelian randomization as a cornerstone of causal inference for gut microbiota and related diseases from the perspective of bibliometrics.

Gut microbiota, a special group of microbiotas in the human body, contributes to health in a way that can't be ignored. In recent years, Mendelian randomization, which is a widely used and successful method of analyzing causality, has been investigated for the relationship between the gut microbiota and related diseases. Unfortunately, there seems to be a shortage of systematic bibliometric analysis in this field. Therefore, this study aims to investigate the research progress of Mendelian randomization for gut microbiota through comprehensive bibliometric analysis. In this study, publications about Mendelian randomization for gut microbiota were gathered from 2013 to 2023, utilizing the Web of Science Core Collection as our literature source database. The search strategies were as follows: TS = (intestinal flora OR gut flora OR intestinal microflora OR gut microflora OR intestinal microbiota OR gut microbiota OR bowel microbiota OR bowel flora OR gut bacteria OR intestinal tract bacteria OR bowel bacteria OR gut metabolites OR gut microbiota) and TS = (Mendelian randomization). VOSviewer (version 1.6.18), CiteSpace (version 6.1.R1), Microsoft Excel 2021, and Scimago Graphica were employed for bibliometric and visualization analysis. According to research, from January 2013 to August 2023, 154 publications on Mendelian randomization for gut microbiota were written by 1053 authors hailing from 332 institutions across 31 countries and published in 86 journals. China had the highest number of publications, with 109. Frontiers in Microbiology is the most prolific journal, and Lei Zhang has published the highest number of significant articles. The most popular keywords were "Mendelian randomization," "gut microbiota," "instruments," "association," "causality," "gut microbiome," "risk," "bias," "genome-wide association," and "causal relationship." Moreover, the current research hotspots in this field focus on utilizing a 2-sample Mendelian randomization to investigate the relationship between gut microbiota and associated disorders. This research systematically reveals a comprehensive overview of the literature that has been published over the last 10 years about Mendelian randomization for gut microbiota. Moreover, the knowledge of key information in the field from a bibliometric perspective may greatly facilitate future research in the field.

The impact of educational attainment on mental health: A Causal Assessment from the UKB and FinnGen Cohorts.

Observational studies report inverse associations between educational attainment and depression/anxiety risks, but confounding hinders causal inference. This study aimed to assess potential causal relationships using Mendelian randomization (MR). Two-sample MR analysis was conducted using genetic instruments for education, smoking, body mass index, and physical activity from published genome-wide association studies. Depression and anxiety data came from the UK Biobank ([UKB] 117,782 individuals) and FinnGen (215,644 individuals) cohorts. Inverse variance weighted regression determined associations between exposures and mental health outcomes. Increased educational attainment was causally associated with reduced risks of depression (odds ratio [OR] = 0.99 per year, 95% confidence interval [CI]: 0.990-0.996, P < .001) and anxiety (OR = 0.99, CI: 0.98-0.991, P < .001) in both cohorts. Smoking initiation conferred higher risks of depression (UKB OR = 1.05, CI: 1.03-1.06, P < .001; FinnGen OR = 1.20, CI: 1.10-1.32, P < .001) and anxiety (FinnGen only, OR = 1.10, CI: 1.01-1.21, P < .05). Likewise, maternal smoking history associated with greater depression (UKB OR = 1.15, CI: 1.10-1.35, P = .027) and anxiety susceptibility (FinnGen OR = 3.02, CI: 1.67-5.46, P = .011). Higher body mass index elevated depression risk in both cohorts. Physical activity showed no clear associations. This MR study provides evidence that education may causally reduce mental health disorder risk. Smoking, obesity, and low activity appear detrimentally linked to depression and anxiety. Improving access to education could offer effective strategies for lowering population psychiatric burden.

Causal associations between psoriasis, eczema, urticaria, and mental illness: A bidirectional Mendelian randomization study of the European population.

Observational studies have reported a relationship between multiple common dermatoses and mental illness. To assess the potential bidirectional causality between 3 skin disorders (psoriasis, eczema, and urticaria) and 4 psychiatric disorders (bipolar disorder, schizophrenia, major depressive disorder, and anxiety) in the European population, we used Mendelian randomization (MR) analysis, which provides definitive evidence for causal inference. Eligible single nucleotide polymorphisms were screened for dermatological and psychiatric disorders using a genome-wide association study database. We conducted bidirectional, 2-sample MR analysis using instrumental variables related to psoriasis, eczema, and urticaria as exposure factors, and bipolar disorder, schizophrenia, major depression, and anxiety as outcomes. Reverse MR analysis with bipolar disorder, schizophrenia, major depression, and anxiety as exposure and psoriasis, eczema, and urticaria as outcomes were also performed, and the causality was analyzed using inverse-variance weighting (IVW), MR-Egger, and weighted median methods. To thoroughly assess causality, sensitivity analyses were conducted using the IVW, MR-PRESSO, and MR-Egger methods. The results showed that bipolar disorder increased the incidence of psoriasis (odds ratio = 1.271, 95% confidence interval = 1.003-1.612, P = .047), heterogeneity test with Cochran Q test in the IVW showed P value > .05, (P = .302), the MR-Pleiotropy and MR-PRESSO (outlier methods) in the multiplicity test showed P value > .05, (P = .694; P = .441), and MR-Pleiotropy evidence showed no apparent intercept (intercept = -0.060; SE = 0.139; P = .694). Major depression increased the risk of eczema (odds ratio = 1.002, 95% confidence interval = 1.000-1.004, P = .024), heterogeneity test showed P value > .05, (P = .328), multiplicity detection showed P value > .05, (P = .572; P = .340), and MR-Pleiotropy evidence showed no apparent intercept (intercept = -0.099; SE = 0.162; P = .572). Sensitivity analyses of the above results were reliable, and no heterogeneity or multiplicity was found. This study demonstrated a statistically significant causality between bipolar disorder and psoriasis, major depression, and eczema in a European population, which could provide important information for physicians in the clinical management of common skin conditions.

Causality Investigation between Gut Microbiome and Sleep-Related Traits: A Bidirectional Two-Sample Mendelian Randomization Study.

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a bidirectional two-sample Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386,577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method, alongside a Bonferroni correction ((p < 2.4 × 10-4) to determine significant causal associations. We further applied Cochran's Q statistical analysis, MR-Egger intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) for heterogeneity and pleiotropy assessment. IVW estimates revealed 79 potential causal effects of microbial taxa and pathways on sleep-related traits and 45 inverse causal relationships, with over half related to pathways, emphasizing their significance. The results revealed two significant causal associations: genetically determined relative abundance of pentose phosphate decreased sleep duration (p = 9.00 × 10-5), and genetically determined increase in fatty acid level increased the ease of getting up in the morning (p = 8.06 × 10-5). Sensitivity analyses, including heterogeneity and pleiotropy tests, as well as a leave-one-out analysis of single nucleotide polymorphisms, confirmed the robustness of these relationships. This study explores the potential causal relationships between sleep and microbial taxa and pathways, offering novel insights into their complex interplay.

Assessing causal associations of bile acids with obesity indicators: A Mendelian randomization study.

Maintaining a balanced bile acids (BAs) metabolism is essential for lipid and cholesterol metabolism, as well as fat intake and absorption. The development of obesity may be intricately linked to BAs and their conjugated compounds. Our study aims to assess how BAs influence the obesity indicators by Mendelian randomization (MR) analysis. Instrumental variables of 5 BAs were obtained from public genome-wide association study databases, and 8 genome-wide association studies related to obesity indicators were used as outcomes. Causal inference analysis utilized inverse-variance weighted (IVW), weighted median, and MR-Egger methods. Sensitivity analysis involved MR-PRESSO and leave-one-out techniques to detect pleiotropy and outliers. Horizontal pleiotropy and heterogeneity were assessed using the MR-Egger intercept and Cochran Q statistic, respectively. The IVW analysis revealed an odds ratio of 0.94 (95% confidence interval: 0.88, 1.00; P = .05) for the association between glycolithocholate (GLCA) and obesity, indicating a marginal negative causal association. Consistent direction of the estimates obtained from the weighted median and MR-Egger methods was observed in the analysis of the association between GLCA and obesity. Furthermore, the IVW analysis demonstrated a suggestive association between GLCA and trunk fat percentage, with a beta value of -0.014 (95% confidence interval: -0.027, -0.0004; P = .04). Our findings suggest a potential negative causal relationship between GLCA and both obesity and trunk fat percentage, although no association survived corrections for multiple comparisons. These results indicate a trend towards a possible association between BAs and obesity, emphasizing the need for future studies.