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.

SLE: a cognitive step forward-a synthesis of rethinking theories, causality, and ignored DNA structures.

Systemic lupus erythematosus (SLE) is classified by instinctual classification criteria. A valid proclamation is that these formally accepted SLE classification criteria legitimate the syndrome as being difficult to explain and therefore enigmatic. SLE involves scientific problems linked to etiological factors and criteria. Our insufficient understanding of the clinical condition uniformly denoted SLE depends on the still open question of whether SLE is, according to classification criteria, a well-defined one disease entity or represents a variety of overlapping indistinct syndromes. Without rational hypotheses, these problems harm clear definition(s) of the syndrome. Why SLE is not anchored in logic, consequent, downstream interdependent and interactive inflammatory networks may rely on ignored predictive causality principles. Authoritative classification criteria do not reflect consequent causality criteria and do not unify characterization principles such as diagnostic criteria. We need now to reconcile legendary scientific achievements to concretize the delimitation of what SLE really is. Not all classified SLE syndromes are "genuine SLE"; many are theoretically "SLE-like non-SLE" syndromes. In this study, progressive theories imply imperative challenges to reconsider the fundamental impact of "the causality principle". This may offer us logic classification and diagnostic criteria aimed at identifying concise SLE syndromes as research objects. Can a systems science approach solve this problem?

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.

Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression.

The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.

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.

Psychological distress and uterine fibroids: a bidirectional two-sample mendelian randomization study.

BACKGROUND: Observational data indicates a connection between emotional discomfort, such as anxiety and depression, and uterine fibroids (UFs). However, additional investigation is required to establish the causal relationship between them. Hence, we assessed the reciprocal causality between four psychological disorders and UFs utilizing two-sample Mendelian randomization (MR). METHODS: To evaluate the causal relationship between four types of psychological distress (depressive symptoms, severe depression, anxiety or panic attacks, mood swings) and UFs, bidirectional two-sample MR was employed, utilizing single nucleotide polymorphisms (SNPs) associated with these conditions. Both univariate MR (UVMR) and multivariate MR (MVMR) primarily applied inverse variance weighted (IVW) as the method for estimating potential causal effects. Complementary approaches such as MR Egger, weighted median, simple mode, and weighted mode were utilized to validate the findings. To assess the robustness of our MR results, we conducted sensitivity analyses using Cochran's Q-test and the MR Egger intercept test. RESULTS: The results of our UVMR analysis suggest that genetic predispositions to depressive symptoms (Odds Ratio [OR] = 1.563, 95% Confidence Interval [CI] = 1.209-2.021, P = 0.001) and major depressive disorder (MDD) (OR = 1.176, 95% CI = 1.044-1.324, P = 0.007) are associated with an increased risk of UFs. Moreover, the IVW model showed a nominally significant positive correlation between mood swings (OR: 1.578; 95% CI: 1.062-2.345; P = 0.024) and UFs risk. However, our analysis did not establish a causal relationship between UFs and the four types of psychological distress. Even after adjusting for confounders like body mass index (BMI), smoking, alcohol consumption, and number of live births in the MVMR, the causal link between MDD and UFs remained significant (OR = 1.217, 95% CI = 1.039-1.425, P = 0.015). CONCLUSIONS: Our study presents evidence supporting the causal relationship between genetic susceptibility to MDD and the incidence of UFs. These findings highlight the significance of addressing psychological health issues, particularly depression, in both the prevention and treatment of UFs.

The causal relationship between blood metabolites and rosacea: A Mendelian randomization.

BACKGROUND: An increasing amount of research demonstrates that metabolic disorders are related to rosacea. However, the correlations and causal relationships among them remain unknown. METHODS: We conducted not only forward 2-sample MR (Mendelian randomization) analyses but also reverse MR analyses which showed positive results in the forward MR analysis. In the forward MR analyses, inverse-variance weighted (IVW) and MR-Egger were performed as MR analyses. Cochran's Q test and the MR-Egger Intercept were used for sensitivity analyses. Concerning reverse MR analyses, IVW, MR-Egger, weighted median, simple mode, and weighted mode were applied. Cochran's Q test, MR-Egger Intercept, and MR pleiotropy residual sum and outlier (MR-PRESSO) outlier test were applied as sensitivity analyses. RESULTS: A total of 24 metabolites and 1 metabolite ratio were shown to have a causal effect on rosacea. N-lactoyl phenylalanine (N-Lac-Phe) was estimated as statistically significant by Bonferroni correction. Interestingly, we found three metabolites that were negatively associated with rosacea, especially caffeine, which are in line with the results of a large cohort study of females. For reverse MR analysis, we revealed that rosacea could potentially decrease the generation of two metabolites: octadecenedioate (C18:1-DC) and methyl vanillate sulfate. CONCLUSION: This study identified blood metabolites that may be associated with the development of rosacea. However, the exact mechanism by which these positive metabolites influence rosacea remains uncertain due to the paucity of experimental investigations. The combination of genetics and metabolomics offers novel viewpoints on the research of underlying mechanisms of rosacea and has significant value in screening and prevention of rosacea.

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.

Investigating causal associations between pneumonia and lung cancer using a bidirectional mendelian randomization framework.

BACKGROUND: Pneumonia and lung cancer are both major respiratory diseases, and observational studies have explored the association between their susceptibility. However, due to the presence of potential confounders and reverse causality, the comprehensive causal relationships between pneumonia and lung cancer require further exploration. METHODS: Genome-wide association study (GWAS) summary-level data were obtained from the hitherto latest FinnGen database, COVID-19 Host Genetics Initiative resource, and International Lung Cancer Consortium. We implemented a bidirectional Mendelian randomization (MR) framework to evaluate the causal relationships between several specific types of pneumonia and lung cancer. The causal estimates were mainly calculated by inverse-variance weighted (IVW) approach. Additionally, sensitivity analyses were also conducted to validate the robustness of the causalty. RESULTS: In the MR analyses, overall pneumonia demonstrated a suggestive but modest association with overall lung cancer risk (Odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.01 - 1.44, P = 0.037). The correlations between specific pneumonia types and overall lung cancer were not as significant, including bacterial pneumonia (OR: 1.07, 95% CI: 0.91 - 1.26, P = 0.386), viral pneumonia (OR: 1.00, 95% CI: 0.95 - 1.06, P = 0.891), asthma-related pneumonia (OR: 1.18, 95% CI: 0.92 - 1.52, P = 0.181), and COVID-19 (OR: 1.01, 95% CI: 0.78 - 1.30, P = 0.952). Reversely, with lung cancer as the exposure, we observed that overall lung cancer had statistically crucial associations with bacterial pneumonia (OR: 1.08, 95% CI: 1.03 - 1.13, P = 0.001) and viral pneumonia (OR: 1.09, 95% CI: 1.01 - 1.19, P = 0.037). Sensitivity analysis also confirmed the robustness of these findings. CONCLUSION: This study has presented a systematic investigation into the causal relationships between pneumonia and lung cancer subtypes. Further prospective study is warranted to verify these findings.

Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.

Causal inference in the absence of positivity: The role of overlap weights.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

Guide to Decomposition of Causal Effects Into Mediation, Interaction, and Direct Effects: Case Study on Aerobic Exercise and Parkinson Disease.

Mediation analysis can be applied in medical research with the aim of understanding the pathways that operate between an exposure and its effects on an outcome. This method can help to improve our understanding of pathophysiologic mechanisms and may guide the choice of potential treatment strategies. Traditional mediation analysis decomposes the total effect of an intervention on the outcome into 2 effects: (1) an indirect effect, from exposure using a mediator to the outcome, and (2) a direct effect, directly from exposure to outcome. A limitation of this method is that it assumes no interaction between the exposure and the mediator, which can either lead to an over- or underestimation of clinically relevant effects. The "4-way decomposition" method has the advantage of overcoming this limitation. Specifically, the total effect of an exposure on the outcome is decomposed into 4 elements: (1) reference interaction (interaction only), (2) mediated interaction (mediation and interaction), (3) the pure indirect effect (mediation but not interaction), and (4) the direct effect (no mediation and no interaction). We provide a guide to select the most appropriate method to investigate and decompose any causal effect given the research question at hand. We explain the application of the 4-way decomposition and illustrate this with a real-world example of how aerobic exercise may influence motor function in persons with Parkinson disease.

Estimating the causal effects of income on health: how researchers' definitions of "income" matter.

BACKGROUND: There is a well-established cross-sectional association between income and health, but estimates of the causal effects of income vary substantially. Different definitions of income may lead to substantially different empirical results, yet research is often framed as investigating "the effect of income" as if it were a single, easily definable construct. METHODS/RESULTS: The aim of this paper is to introduce a taxonomy for definitional and conceptual issues in studying individual- or household-level income for health research. We focus on (1) the definition of the income measure (earned and unearned; net, gross, and disposable; real and nominal; individual and household; relative and absolute income) and (2) the definition of the causal contrast (amount, functional form assumptions/transformations, direction, duration of change, and timing of exposure and follow-up). We illustrate the application of the taxonomy to four examples from the published literature. CONCLUSIONS: Quantified estimates of causal effects of income on health and wellbeing have crucial relevance for policymakers to anticipate the consequences of policies targeting the social determinants of health. However, much prior evidence has been limited by lack of clarity in distinguishing between different causal questions. The present framework can help researchers explicitly and precisely articulate income-related exposures and causal questions.

Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges in clarifying the estimand and underlying assumptions. This article proposes expressing causal structures using graphical tools-directed acyclic graphs (DAGs) and single-world intervention graphs (SWIGs)-in the planning phase of a clinical trial. It presents five rules for selecting a sufficient set of adjustment variables on the basis of a diagram representing the clinical trial, along with three case studies of randomized and single-arm trials and a brief tutorial on DAG and SWIG. Through the case studies, DAGs appear effective in clarifying assumptions for identifying causal effects, although SWIGs should complement DAGs due to their limitations in the presence of intercurrent events in oncology research.

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.

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.

A Note on Modelling Bidirectional Feedback Loops in Mendelian Randomization Studies.

Structural equation models (SEMs) involving feedback loops may offer advantages over standard instrumental variables estimators in terms of modelling causal effects in the presence of bidirectional relationships. In the following note, we show that in the case of a single "exposure" and "outcome" variable, modelling relationships using a SEM with a simple bidirectional linear feedback loop offers no advantage over traditional instrumental variables estimators in terms of consistency (i.e. both approaches yield consistent estimates of the causal effect, provided that causal estimates are obtained in both directions). In the case of finite samples, traditional IV estimators and SEM exhibited similar power across many of the conditions we examined, although which method performed best depended on the residual correlation between variables and the strength of the instruments. In particular, the power of SEM was insensitive to the residual correlation between variables, whereas the power of the Wald estimator/2SLS improved (deteriorated) relative to SEM as the magnitude of the residual correlation increased (decreased) assuming a positive causal effect of the exposure on the outcome. The power of SEM improved relative to the Wald estimator/2SLS as the instruments explained more residual variance in the "outcome" variable.

Exploring the causal effects of depression and antidepressants on COVID-19.

BACKGROUND: While existing studies have suggested an increased risk of COVID-19 in patients with depression, the causal impact of MDD on the severity of COVID-19 remains to be validated. Additionally, the potential impact of antidepressant medication on the risk of COVID-19 is not known. METHODS: In our study, we applied a Mendelian Randomization (MR) method, leveraging summary data from GWAS, to evaluate the potential causal effects of depression on three COVID-19 outcomes. Furthermore, we investigated the causal effects of antidepressants on COVID-19 outcomes. The COVID-19 datasets contain information on various stages of the disease, including SARS-CoV-2 infection (N = 2,597,856), hospitalized COVID-19 (N = 2,095,324), and critical COVID-19 (N = 1,086,211). Datasets for depression and antidepressants were comprised of 1,349,887 and 106,785 participants, respectively. RESULTS: Employing the inverse variance-weighted (IVW) method, we show a causal association between depression and three COVID-19 outcomes. Specifically, we found that genetic liability to depression is linked to critical COVID-19 (OR: 1.28, 95 % CI: 1.13-1.46), hospitalized COVID-19 (OR: 1.23, 95 % CI: 1.13-1.34), and SARS-CoV-2 infection (OR: 1.06, 95 % CI: 1.02-1.10). Interestingly, the use of antidepressants was not associated with COVID-19, with the odds ratios for critical COVID-19 (OR: 1.05, 95 % CI: 0.88-1.26), hospitalization (OR: 1.01, 95 % CI: 0.90-1.13), and SARS-CoV-2 infection (OR: 1.03, 95 % CI: 0.99-1.08) indicating no causal impact. CONCLUSION: Our study indicates that genetic liability to depression may increase the susceptibility to COVID-19 and its severe forms. The lack of causal effect of antidepressant use on COVID-19 implies antidepressant medication may counteract the detrimental effect of depression on COVID-19.