BACKGROUND: Genome-wide association studies have enabled Mendelian randomization analyses to be performed at an industrial scale. Two-sample summary data Mendelian randomization analyses can be performed using publicly available data by anyone who has access to the internet. While this has led to many insightful papers, it has also fuelled an explosion of poor-quality Mendelian randomization publications, which threatens to undermine the credibility of the whole approach.
RESULTS: We detail five pitfalls in conducting a reliable Mendelian randomization investigation: (1) inappropriate research question, (2) inappropriate choice of variants as instruments, (3) insufficient interrogation of findings, (4) inappropriate interpretation of findings, and (5) lack of engagement with previous work. We have provided a brief checklist of key points to consider when performing a Mendelian randomization investigation; this does not replace previous guidance, but highlights critical analysis choices. Journal editors should be able to identify many low-quality submissions and reject papers without requiring peer review. Peer reviewers should focus initially on key indicators of validity; if a paper does not satisfy these, then the paper may be meaningless even if it is technically flawless.
CONCLUSIONS: Performing an informative Mendelian randomization investigation requires critical thought and collaboration between different specialties and fields of research.

OBJECTIVE: Observational studies have established a connection between gut microbiota and ankylosing spondylitis (AS) risk; however, whether the observed associations are causal remains unclear. Therefore, we conducted a two-sample Mendelian randomization (MR) analysis to assess the potential causal associations of gut microbiota with AS risk.
METHODS: Instrumental variants of gut microbiota were obtained from the MiBioGen consortium (n = 18,340) and the Dutch Microbiome Project (n = 7738). The FinnGen consortium provided genetic association summary statistics for AS, encompassing 2860 cases and 270,964 controls. We used the inverse-variance weighted (IVW) method as the primary analysis, supplemented with the weighted median method, maximum likelihood-based method, MR pleiotropy residual sum and outlier test, and MR-Egger regression. In addition, we conducted a reverse MR analysis to assess the likelihood of reverse causality.
RESULTS: After the Bonferroni correction, species Bacteroides vulgatus remained statistically significantly associated with AS risk (odds ratio (OR) 1.55, 95% confidence interval (CI) 1.22-1.95, P = 2.55 × 10-4). Suggestive evidence of associations of eleven bacterial traits with AS risk was also observed (P < 0.05 by IVW). Among them, eight were associated with an elevated AS risk (OR 1.37, 95% CI 1.07-1.74, P = 0.011 for phylum Verrucomicrobia; OR 1.31, 95% CI 1.03-1.65, P = 0.026 for class Verrucomicrobiae; OR 1.17, 95% CI 1.01-1.36, P = 0.035 for order Bacillales; OR 1.31, 95% CI 1.03-1.65, P = 0.026 for order Verrucomicrobiales; OR 1.43, 95% CI 1.13-1.82, P = 0.003 for family Alcaligenaceae; OR 1.31, 95% CI 1.03-1.65, P = 0.026 for family Verrucomicrobiaceae; OR 1.31, 95% CI 1.03-1.65, P = 0.026 for genus Akkermansia; OR 1.55, 95% CI 1.19-2.02, P = 0.001 for species Sutterella wadsworthensis). Three traits exhibited a negative association with AS risk (OR 0.68, 95% CI 0.53-0.88, P = 0.003 for genus Dialister; OR 0.84, 95% CI 0.72-0.97, P = 0.020 for genus Howardella; OR 0.75, 95% CI 0.59-0.97, P = 0.026 for genus Oscillospira). Consistent associations were observed when employing alternate MR methods. In the reverse MR, no statistically significant correlations were detected between AS and these bacterial traits.
CONCLUSIONS: Our results revealed the associations of several gut bacterial traits with AS risk, suggesting a potential causal role of gut microbiota in AS development. Nevertheless, additional research is required to clarify the mechanisms by which these bacteria influence AS risk. Key Points • The association of gut microbiota with AS risk in observational studies is unclear. • This MR analysis revealed associations of 12 gut bacterial traits with AS risk.

UNASSIGNED: Previous observational studies have hinted at a potential correlation between aplastic anemia (AA) and the gut microbiome. However, the precise nature of this bidirectional causal relationship remains uncertain.
UNASSIGNED: We conducted a bidirectional two-sample Mendelian randomization (MR) study to investigate the potential causal link between the gut microbiome and AA. Statistical analysis of the gut microbiome was based on data from an extensive meta-analysis (genome-wide association study) conducted by the MiBioGen Alliance, involving 18,340 samples. Summary statistical data for AA were obtained from the Integrative Epidemiology Unit database. Single -nucleotide polymorphisms (SNPs) were estimated and summarized using inverse variance weighted (IVW), MR Egger, and weighted median methods in the bidirectional MR analysis. Cochran\'s Q test, MR Egger intercept test, and sensitivity analysis were employed to assess SNP heterogeneity, horizontal pleiotropy, and stability.
UNASSIGNED: The IVW analysis revealed a significant correlation between AA and 10 bacterial taxa. However, there is currently insufficient evidence to support a causal relationship between AA and the composition of gut microbiome.
UNASSIGNED: This study suggests a causal connection between the prevalence of specific gut microbiome and AA. Further investigation into the interaction between particular bacterial communities and AA could enhance efforts in prevention, monitoring, and treatment of the condition.

UNASSIGNED: This Mendelian Randomization (MR) study aims to explore the potential causal relationships between four sleep traits and pain in 10 different body sites.
UNASSIGNED: The study utilizes exposure and outcome data from the GWAS database, employing the Inverse Variance Weighting Method (IVW) for primary causal estimates. Cochran Q and Rücker Q heterogeneity tests are conducted using IVW and MR-Egger methods, with the Egger-intercept method for pleiotropy testing, leave-one-out sensitivity analysis, and calculation of F-statistics to assess the presence of weak instrument bias.
UNASSIGNED: The study reveals that genetically predicted insomnia significantly increases the risk of unspecified pain, chest pain, gum pain, upper abdominal pain, and lower abdominal pain occurrence. Daytime napping is associated with a moderate reduction in the likelihood of joint pain but may concomitantly elevate the risk of chest pain, upper abdominal pain, and generalized abdominal pain. Neither sleep chronotype nor sleep duration demonstrated a definitive causal relationship with pain perception.
UNASSIGNED: This study elucidates the causal relationships between four sleep characteristics and pain across 10 different body regions. Overall, the contribution of insomnia and sleep deficiency to pain in multiple body regions is more pronounced. Conversely, the association between adequate sleep and the likelihood of somatic pain is relatively lower and less significant.

暂无摘要。

OBJECTIVE: Gastroesophageal reflux disease (GERD) and temporomandibular joint disorder (TMD) are relatively common conditions with a potential causal relationship. This study aims to investigate the possible causal relationship between GERD and TMD through bidirectional Mendelian randomization analysis.
METHODS: Using data from large GWAS databases, we conducted bidirectional Mendelian randomization analyses to investigate the potential causal link between GERD and TMD. Instrumental variables were selected from the IEU platform, comprising 129,080 GERD cases and 473,524 controls from the UK Biobank. TMD data from the FinnGen project included 6,314 cases and 222,498 controls.
RESULTS: The forward MR analysis suggested that GERD may increase the risk of TMD (OR = 1.47, 95% CI: 1.20-1.81, P = 2e-4). The Weighted Median method also yielded significant results (OR = 1.53, 95% CI: 1.14-2.04, P = 4.1e-3). However, the reverse MR analysis did not reveal a significant association between TMD and GERD (OR = 1.02, 95% CI: 0.98-1.05, P = .33).
CONCLUSIONS: This study, employing MR analysis, provides initial evidence supporting a potential causal relationship between GERD and TMD. The findings contribute to a better understanding of the relationship between these two conditions and offer insights for future clinical investigations.
CONCLUSIONS: The findings of this study hold potential clinical significance in guiding early management strategies for GERD, reducing the incidence of TMD, and optimizing healthcare resource allocation, thereby improving patient quality of life. Further clinical studies are warranted to validate these findings and explore underlying mechanisms.

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

BACKGROUND: PM2.5 can induce and aggravate the occurrence and development of cardiovascular diseases (CVDs). The objective of our study is to estimate the causal effect of PM2.5 on mortality rates associated with CVDs using the instrumental variables (IVs) method.
METHODS: We extracted daily meteorological, PM2.5 and CVDs death data from 2016 to 2020 in Binzhou. Subsequently, we employed the general additive model (GAM), two-stage predictor substitution (2SPS), and control function (CFN) to analyze the association between PM2.5 and daily CVDs mortality.
RESULTS: The 2SPS estimated the association between PM2.5 and daily CVDs mortality as 1.14% (95% CI: 1.04%, 1.14%) for every 10 µg/m3 increase in PM2.5. Meanwhile, the CFN estimated this association to be 1.05% (95% CI: 1.02%, 1.10%). The GAM estimated it as 0.85% (95% CI: 0.77%, 1.05%). PM2.5 also exhibited a statistically significant effect on the mortality rate of patients with ischaemic heart disease, myocardial infarction, or cerebrovascular accidents (P < 0.05). However, no significant association was observed between PM2.5 and hypertension.
CONCLUSIONS: PM2.5 was significantly associated with daily CVDs deaths (excluding hypertension). The estimates from the IVs method were slightly higher than those from the GAM. Previous studies based on GAM may have underestimated the impact of PM2.5 on CVDs.

The endogeneity problem arises when the auxiliary variables correlate to the error terms. In such cases, appropriate instrumental variables ensure efficient estimation. Calibration has recognized itself as an important methodological tool at a large scale to estimate the population total in survey sampling. Which does not offer efficient estimation in the presence of endogeneity. When endogeneity is present in the auxiliary variables, the calibration using endogenous auxiliary variables may produce biasedness and increase variance due to inappropriate model assumptions. In this article, we propose instrumental-variable calibrated estimators by using the classical instrumental-variables approach for the case of exact identification that are more efficient than conventional calibration estimators when some auxiliary variables are endogenous. The necessary properties of the proposed estimators are presented. Our study is backed by both the simulation study and a real data example to check the performance of the proposed estimators.

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