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.

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.

This research analyzes the impact of education quality on GDP per capita and educational access, using instrumental variables (IV) models. The findings shed light on the potential of education investment to foster economic growth and development, highlighting the importance of targeted interventions, teacher training, and data-driven approaches to improve educational outcomes and reduce access disparities. Policymakers and international organizations can utilize these insights to devise strategies aligned with Sustainable Development Goal 4 (SDG 4) of ensuring inclusive and equitable education for all, potentially contributing to the achievement of this crucial goal.

BACKGROUND: Evidence indicates that the risk of developing a secondary ovarian cancer (OC) is correlated with estrogen receptor (ER) status. However, the clinical significance of the relationship between ER-associated breast cancer (BC) and clear cell ovarian cancer (CCOC) remains elusive.
METHODS: Independent single nucleotide polymorphisms (SNPs) strongly correlated with exposure were extracted, and those associated with confounders and outcomes were removed using the PhenoScanner database. SNP effects were extracted from the outcome datasets with minor allele frequency > 0.01 as the filtration criterion. Next, valid instrumental variables (IVs) were obtained by harmonizing exposure and outcome effects and further filtered based on F-statistics (> 10). Mendelian randomization (MR) assessment of valid IVs was carried out using inverse variance weighted (IVW), MR Egger (ME), weighted median (WM), and multiplicative random effects-inverse variance weighted (MRE-IVW) methods. For sensitivity analysis and visualization of MR findings, a heterogeneity test, a pleiotropy test, a leave-one-out test, scatter plots, forest plots, and funnel plots were employed.
RESULTS: MR analyses with all four methods revealed that CCOC was not causally associated with ER-negative BC (IVW results: odds ratio (OR) = 0.89, 95% confidence interval (CI) = 0.66-1.20, P = 0.431) or ER-positive BC (IVW results: OR = 0.99, 95% CI = 0.88-1.12, P = 0.901). F-statistics were computed for each valid IV, all of which exceeded 10. The stability and reliability of the results were confirmed by sensitivity analysis.
CONCLUSIONS: Our findings indicated that CCOC dids not have a causal association with ER-associated BC. The absence of a definitive causal link between ER-associated BC and CCOC suggested a minimal true causal influence of ER-associated BC exposure factors on CCOC. These results indicated that individuals afflicted by ER-associated BC could alleviate concerns regarding the developing of CCOC, thereby aiding in preserving their mental well-being stability and optimizing the efficacy of primary disease treatment.

Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.

We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments-possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.

UNASSIGNED: Previous observational studies have identified a correlation between elevated plasma homocysteine (Hcy) levels and polycystic ovary syndrome (PCOS). This study aimed to determine whether a causal relationship exists between Hcy and PCOS at the genetic level.
UNASSIGNED: A two-sample Mendelian Randomization (TSMR) study was implemented to assess the genetic impact of plasma levels of Hcy, folate, vitamin B12, and vitamin B6 on PCOS in individuals of European ancestry. Independent single nucleotide polymorphisms (SNPs) associated with Hcy (n=12), folate (n=2), vitamin B12 (n=10), and vitamin B6 (n=1) at genome-wide significance levels (P<5×10-8) were selected as instrumental variables (IVs). Data concerning PCOS were obtained from the Apollo database. The primary method of causal estimation was inverse variance weighting (IVW), complemented by sensitivity analyses to validate the results.
UNASSIGNED: The study found no genetic evidence to suggest a causal association between plasma levels of Hcy, folate, vitamin B12, vitamin B6, and PCOS. The effect sizes, determined through random-effect IVW, were as follows: Hcy per standard deviation increase, OR = 1.117, 95%CI: (0.842, 1.483), P = 0.442; folate per standard deviation increase, OR = 1.008, CI: (0.546, 1.860), P = 0.981; vitamin B12 per standard deviation increase, OR = 0.978, CI: (0.808, 1.185), P = 0.823; and vitamin B6 per standard deviation increase, OR = 0.967, CI: (0.925, 1.012), P = 0.145. The fixed-effect IVW results for each nutrient exposure and PCOS were consistent with the random-effect IVW findings, with additional sensitivity analyses reinforcing these outcomes.
UNASSIGNED: Our findings indicate no causal link between Hcy, folate, vitamin B12, vitamin B6 levels, and PCOS.