Human causality detection and judgment with unsignaled and signaled delayed outcomes.

Four experiments examined human ratings of causal effectiveness, and ability to detect causal relationships, in a nonverbal paradigm. Participants responded on a concurrent random interval, extinction schedule. In the presence of one stimulus, responses produced an outcome (triangle flash); in the presence of the other stimulus, they did not. Following making a judgment of causal effectiveness, two further stimuli were presented simultaneously with one another, and participants had to select one depending on which of the previous two stimuli were associated with effective responses. In all experiments, immediate outcomes were associated with higher causal ratings and better causal detection than outcomes delayed by 3 s. A signal inserted between response and outcome improved ratings and detection (Experiments 2 and 4), even when it was contiguous with the response but not the outcome (Experiments 2 and 3). Stimuli associated with both components (marking cues) did not impact judgments or detection (Experiment 3). Stimuli signaling the availability of an outcome if a response was made (signaled reinforcement) did not improve causal judgments, but did improve detection of stimuli associated with the outcome (Experiment 4). Responses during the delay interfered with detection of the actual relationship when delays were unsignaled (Experiments 1-4), but not with fully or briefly signaled delays (Experiments 2-4), or with signaled reinforcement (Experiment 4). The results suggest a delay stimulus serves to signal the response has been successful and demark the delay period by serving a discriminative function. These findings mirror those seen in nonhuman conditioning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Causal diagrams for disease latency bias.

BACKGROUND: Disease latency is defined as the time from disease initiation to disease diagnosis. Disease latency bias (DLB) can arise in epidemiological studies that examine latent outcomes, since the exact timing of the disease inception is unknown and might occur before exposure initiation, potentially leading to bias. Although DLB can affect epidemiological studies that examine different types of chronic disease (e.g. Alzheimer's disease, cancer etc), the manner by which DLB can introduce bias into these studies has not been previously elucidated. Information on the specific types of bias, and their structure, that can arise secondary to DLB is critical for researchers, to enable better understanding and control for DLB. DEVELOPMENT: Here we describe four scenarios by which DLB can introduce bias (through different structures) into epidemiological studies that address latent outcomes, using directed acyclic graphs (DAGs). We also discuss potential strategies to better understand, examine and control for DLB in these studies. APPLICATION: Using causal diagrams, we show that disease latency bias can affect results of epidemiological studies through: (i) unmeasured confounding; (ii) reverse causality; (iii) selection bias; (iv) bias through a mediator. CONCLUSION: Disease latency bias is an important bias that can affect a number of epidemiological studies that address latent outcomes. Causal diagrams can assist researchers better identify and control for this bias.

The causality of atrial fibrillation on frailty index: A Mendelian randomization study.

Prior epidemiological research has indicated a possible association between atrial fibrillation (AF) and frailty status. Our study used Mendelian randomization to estimate its causality. The genome-wide association studies for AF were utilized as the exposure for individuals included in the UK Biobank (n = 463,010) and publicly available summary statistics data sets of genome-wide association studies meta-analyses for frailty index in individuals of European descent (n = 175,226) was used as the outcome. The inverse variance weighting method was utilized to evaluate causality. To further confirm the reliability of the results, sensitivity analyses were conducted. The inverse variance weighting analysis indicated that the presence of AF was found to be statistically linked to an increased risk of frailty (odds ratio = 3.017, CI: 1.106-8.232, P = .031). MR-Egger intercept test indicated no pleiotropy (Egger intercept = .002, P = .808). The leave-one-out method indicated that the individual SNPs did not have an impact on the robustness of the findings. The research implies a causal relationship between AF and frailty. Early detection and timely intervention of AF can control the occurrence of frailty.

Causal Relationship between Meat Intake and Biological Aging: Evidence from Mendelian Randomization Analysis.

Existing research indicates that different types of meat have varying effects on health and aging, but the specific causal relationships remain unclear. This study aimed to explore the causal relationship between different types of meat intake and aging-related phenotypes. This study employed Mendelian randomization (MR) to select genetic variants associated with meat intake from large genomic databases, ensuring the independence and pleiotropy-free nature of these instrumental variables (IVs), and calculated the F-statistic to evaluate the strength of the IVs. The validity of causal estimates was assessed through sensitivity analyses and various MR methods (MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode), with the MR-Egger regression intercept used to test for pleiotropy bias and Cochran's Q test employed to evaluate the heterogeneity of the results. The findings reveal a positive causal relationship between meat consumers and DNA methylation PhenoAge acceleration, suggesting that increased meat intake may accelerate the biological aging process. Specifically, lamb intake is found to have a positive causal effect on mitochondrial DNA copy number, while processed meat consumption shows a negative causal effect on telomere length. No significant causal relationships were observed for other types of meat intake. This study highlights the significant impact that processing and cooking methods have on meat's role in health and aging, enhancing our understanding of how specific types of meat and their preparation affect the aging process, providing a theoretical basis for dietary strategies aimed at delaying aging and enhancing quality of life.

Causality between Sarcopenia and Depression: A Bidirectional Mendelian Randomization Study.

BACKGROUND: Numerous observational studies have suggested a correlation between sarcopenia and depression, but the nature of this relationship requires further investigation. METHODS: This study employed bidirectional Mendelian randomization to explore this connection. Data from genome-wide association studies were used, encompassing measures of sarcopenia and mental factors, including depression and emotional states. The initial analysis concentrated on the impact of depression on sarcopenia, and then it examined the reverse relationship. The same methodology was applied to emotional data for validation. RESULTS: The results indicated a reciprocal causation between sarcopenia and depression, even when emotional state data were considered. Various emotions can impact sarcopenia, and in turn, sarcopenia can affect emotions, except subjective well-being. These findings highlight a cyclic deterioration between sarcopenia and depression, with a link to negative emotions and a partially ameliorative effect of subjective well-being on sarcopenia. CONCLUSIONS: In summary, this study sheds light on the interplay between psychiatric factors and sarcopenia, offering insights into intervention and prevention strategies.

Causality between migraine and cardiovascular disease: a bidirectional Mendelian randomization study.

BACKGROUND: While growing evidence suggests a relationship between migraine and cardiovascular disease, the genetic evidence for a causal relationship between migraine and cardiovascular disease is still scarce. Investigating the causal association between migraine and cardiovascular disease is vital. METHODS: We carried out a bidirectional Mendelian randomization (MR) study including discovery samples and replication samples using publicly available genome-wide association study (GWAS) summary datasets and stringent screening instrumental variables. Four different MR techniques-Inverse variance weighted (IVW), MR ‒Egger, weighted median, and weighted mode-as well as various sensitivity analyses-Cochran's Q, IVW radial, leave-one-out (LOO), and MR-PRESSO-were utilized to investigate the causal relationship between cardiovascular disease and migraine. RESULTS: The protective causal effects of genetically predicted migraine on coronary artery disease (OR, 0.881; 95% CI 0.790-0.982; p = 0.023) and ischemic stroke (OR, 0.912; 95% CI 0.854-0.974; p = 0.006) were detected in forward MR analysis but not in any other cardiovascular disease. Consistently, we also discovered protective causal effects of coronary atherosclerosis (OR, 0.865; 95% CI 0.797-0.940; p = 0.001) and myocardial infarction (OR, 0.798; 95% CI 0.668-0.952; p = 0.012) on migraine in reverse MR analysis. CONCLUSION: We found a potential protective effect of migraine on coronary artery disease and ischemic stroke and a potential protective effect of coronary atherosclerosis and myocardial infarction on migraine. We emphasised epidemiological and genetic differences and the need for long-term safety monitoring of migraine medications and future research to improve cardiovascular outcomes in migraine patients.

The causal relationship between androgenetic alopecia and serum uric acid: A bidirectional Mendelian randomization analysis.

BACKGROUND: Despite numerous studies investigating the association between androgenetic alopecia (AGA) and serum uric acid (SUA), the causal relationship between AGA and SUA remains unknown. METHODS: We utilized bidirectional Mendelian randomization (MR) to explore the causality between AGA and SUA. Our study chose single nucleotide polymorphisms associated with genome-wide significance (p < 5×10-8) for the exposure and showing low linkage disequilibrium (R2 < 0.001) as IVs. Various MR methods were employed to evaluate causality, including inverse-variance weighted (IVW), Weighted Median, MR-Egger, Weighted Mode and Simple Mode. Sensitivity analyses were performed to test the robustness of the results. RESULTS: Using the IVW method, we did not find a significant causal relationship between AGA and SUA (OR = 1.00, 95% CI 0.99-1.01; p = 0.451). Similarly, the IVW method did not reveal evidence of causality between SUA and AGA (OR = 0.97, 95% CI = 0.91-1.03; p = 0.301). The results from other methods were consistent with those of the IVW approach. CONCLUSION: The study did not identify a causal relationship between AGA and SUA. Future research should involve larger cohorts and advanced methods to validate the findings and explore the complex interactions between AGA and SUA levels in different populations.

Studying Continuous, Time-varying, and/or Complex Exposures Using Longitudinal Modified Treatment Policies.

This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions that can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples, specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.

Causal relationship between inflammatory skin diseases and immunoglobulin A nephropathy: A Mendelian randomization and enrichment analysis study.

OBJECTIVE: To investigate the causal relationship between inflammatory skin diseases (atopic dermatitis, and psoriasis) and IgA nephropathy using Mendelian randomization and enrichment analysis. METHODS: The instrumental variables (IVs) in the European Bioinformatics Institute (EBI) database were used for two-sample MR analysis. The results of inverse variance weighting (IVW) were used as the main method, the MR-Egger method was used for pleiotropy analysis and the leave-one-out method was used for sensitivity analysis to verify the reliability of the data. Combined with the human genome database GeneCards database and Metascape enrichment analysis. RESULTS: People with AD had an increased risk of IgA nephropathy (IVW: OR = 1.06, 95% CI [1.0002-1.1248], p = 0.0491). Psoriasis and IgA nephropathy (IVW: OR = 0.97, 95% CI [0.9394-1.0055], p = 0.1002) no statistical significance, therefore cannot prove cause-and-effect relationship between. CONCLUSIONS: This study provides evidence that atopic dermatitis is associated with an increased risk of IgA nephropathy, but does not provide evidence that psoriasis is causologically associated with IgA nephropathy. Enrichment analysis suggested a causal relationship between inflammatory skin diseases and IgA nephropathy at the genetic level.

Solving the puzzle of quality of life in cancer: integrating causal inference and machine learning for data-driven insights.

BACKGROUND: Understanding the determinants of global quality of life in cancer patients is crucial for improving their overall well-being. While correlations between various factors and quality of life have been established, the causal relationships remain largely unexplored. This study aimed to identify the causal factors influencing global quality of life in cancer patients and compare them with known correlative factors. METHODS: We conducted a retrospective analysis of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire data, alongside demographic and disease-related features, collected from new cancer patients during their initial visit to an oncology outpatient clinic. Correlations with global quality of life were identified using univariate and multivariate regression analyses. Causal inference analysis was performed using two approaches. First, we employed the Dowhy Python library for causal analysis, incorporating prior information and manual characterization of an acyclic graph. Second, we utilized the Linear Non-Gaussian Acyclic Model (LiNGAM) machine learning algorithm from the Lingam Python library, which automatically generated an acyclic graph without prior information. The significance level was set at p < 0.05. RESULTS: Multivariate analysis of 469 new admissions revealed that disease stage, role functioning, emotional functioning, social functioning, fatigue, pain and diarrhea were linked with global quality of life. The most influential direct causal factors were emotional functioning, social functioning, and physical functioning, while the most influential indirect factors were physical functioning, emotional functioning, and fatigue. Additionally, the most prominent total causal factors were identified as type of cancer (diagnosis), cancer stage, and sex, with total causal effect ratios of -9.47, -4.67, and - 1.48, respectively. The LiNGAM algorithm identified type of cancer (diagnosis), nausea and vomiting and social functioning as significant, with total causal effect ratios of -9.47, -0.42, and 0.42, respectively. CONCLUSIONS: This study identified that causal factors for global quality of life in new cancer patients are distinct from correlative factors. Understanding these causal relationships could provide valuable insights into the complex dynamics of quality of life in cancer patients and guide targeted interventions to improve their well-being.

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.

An atlas of causal association between micronutrients and osteoarthritis.

OBJECTIVE: This study examines the causal relationships between serum micronutrients and site-specific osteoarthritis (OA) using Mendelian Randomization (MR). METHODS: This study performed a two-sample MR analysis to explore causal links between 21 micronutrients and 11 OA outcomes. These outcomes encompass overall OA, seven site-specific manifestations, and three joint replacement subtypes. Sensitivity analyses using MR methods, such as the weighted median, MR-Egger, and MR-PRESSO, assessed potential horizontal pleiotropy and heterogeneity. Genome-wide association summary statistical data were utilized for both exposure and outcome data, including up to 826,690 participants with 177,517 OA cases. All data was sourced from Genome-wide association studies datasets from 2009 to 2023. RESULTS: In the analysis of associations between 21 micronutrients and 11 OA outcomes, 15 showed Bonferroni-corrected significance (P < 0.000216), without significant heterogeneity or horizontal pleiotropy. Key findings include strong links between gamma-tocopherol and spine OA (OR = 1.70), and folate with hand OA in finger joints (OR = 1.15). For joint replacements, calcium showed a notable association with a reduced likelihood of total knee replacement (TKR) (OR = 0.52) and total joint replacement (TJR) (OR = 0.56). Serum iron was significantly associated with an increased risk of total hip replacement (THR) (OR = 1.23), while folate indicated a protective effect (OR = 0.95). Various sex-specific associations were also uncovered. CONCLUSION: These findings underscore the critical role of micronutrients in osteoarthritis, providing valuable insights for preventive care and potential enhancement of treatment outcomes.

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.

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.

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.

No causal relationship between serum urate and neurodegenerative diseases: A Mendelian randomization study.

OBJECTIVE: Observational studies have shown that increased serum urate is associated with a lower risk of neurodegenerative diseases (NDs), but the causality remains unclear. We employed a two-sample Mendelian randomization (MR) approach to assess the causal relationship between serum urate and four common subtypes of NDs, including Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). METHODS: Serum urate data came from the CKDGen Consortium. GWAS data for PD, AD, ALS, and MS were obtained from four databases in the primary analysis and then acquired statistics from the FinnGen consortium for replication and meta-analysis. Inverse variance weighted (IVW), weighted median (WM), and MR-Egger regression methods were applied in the MR analyses. Pleiotropic effects, heterogeneity, and leave-one-out analyses were evaluated to validate the results. RESULTS: There was no evidence for the effect of serum urate on PD (OR: 1.00, 95 % CI: 0.90-1.11, P = 0.97), AD (OR: 1.02, 95 % CI: 1.00-1.04, P = 0.06), ALS (OR: 1.05, 95 % CI: 0.97-1.13, P = 0.22), and MS (OR: 1.01, 95 % CI: 0.89-1.14, P = 0.90) risk when combined with the FinnGen consortium, neither was any evidence of pleiotropy detected between the instrumental variables (IVs). CONCLUSION: The MR analysis suggested that serum urate may not be causally associated with a risk of PD, AD, ALS, and MS.