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Causal mediation analysis investigates the mechanism linking exposure and outcome. Dealing with the impact of unobserved confounders among exposure, mediator and outcome is an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, rendering it difficult to estimate the path-specific effects. In this study, we propose a method (PSE-MR) to identify and estimate path-specific effects of an exposure (e.g. education) on an outcome (e.g. osteoarthritis risk) through multiple causally ordered and non-ordered mediators (e.g. body mass index and pack-years of smoking) using summarized genetic data, when the sequential ignorability assumption is violated. Specifically, PSE-MR requires a specific rank condition in which the number of instrumental variables is larger than the number of mediators. Furthermore, we illustrate the utility of PSE-MR by providing guidance for practitioners and exploring the mediation effects of body mass index and pack-years of smoking in the causal pathways from education to osteoarthritis risk. Additionally, the results of simulation reveal that the causal estimates of path-specific effects are almost unbiased with good coverage and Type I error properties. Also, we summarize the least number of instrumental variables for the specific number of mediators to achieve 80% power. [ABSTRACT FROM AUTHOR]

Background: Controlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method.Methods: We are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders.Results: The simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99-2.93) mmol/L with per 1- kg/m2 higher BMI and DBP increased 0.37 (95% CI: 0.03-0.76) mmol/L with per 1- kg/m2 higher BMI. Moreover, 1- kg/m2 increase in BMI was causally associated with a 1.61 (95% CI: 0.96-2.97) mmol/L increase in TC, a 1.66 (95% CI: 0.91-55.30) mmol/L increase in TG and a 2.01 (95% CI: 1.09-4.31) mmol/L increase in LDL. However, BMI was not causally associated with HDL with effect value - 0.20 (95% CI: - 1.71-1.44). And, the effect value of FBG per 1- kg/m2 higher BMI was 0.56 (95% CI: - 0.24-2.18).Conclusions: We propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access. [ABSTRACT FROM AUTHOR]

In many empirical studies, there exist rich individual studies to separately estimate causal effect of the treatment or exposure variable on the outcome variable, but incomplete confounders are adjusted in each study. Suppose we are interested in the causal effect of a treatment or exposure on an outcome variable, and we have available rich datasets that contain different confounders. How to integrate summary-level statistics from multiple individual datasets to improve causal inference has become a main challenge in data fusion. We propose a novel method in this article to identify the causal effect of a treatment or exposure on the continuous outcome. We show that the causal effect is identifiable and can be estimated by combining summary-level statistics from multiple datasets containing subsets of confounders and an external dataset only containing complete confounding information. Simulation studies indicate the unbiasedness of causal effect estimate by our method and we apply our method to a study about the effect of body mass index on fasting blood glucose. [ABSTRACT FROM AUTHOR]

Background and purpose: Previous studies have found ischemic stroke is associated with atrial fibrillation. However, the causal association between ischemic stroke and atrial fibrillation is not clear. Furthermore, the network relationship among ischemic stroke, atrial fibrillation and its risk factors need further attention. This study aims to examine the potential causal association between ischemic stroke and atrial fibrillation and further to explore potential mediators in the causal pathway from ischemic stroke to atrial fibrillation. Methods: Summary statistics from the ISGC (case = 10,307 and control = 19,326) were used as ischemic stroke genetic instruments, AFGen Consortium data (case = 65,446 and control = 522,744) were used for atrial fibrillation, and other consortia data were used for potential mediators (fasting insulin, white blood cell count, procalcitonin, systolic and diastolic blood pressure, body mass index, waist circumference, and height). Under the framework of network Mendelian randomization, two-sample Mendelian randomization study was performed using summary statistics from several genome-wide association studies. Inverse-variance weighted method was performed to estimate causal effect. Results: Blood pressure mediates the causal pathways from ischemic stroke to atrial fibrillation. The total odds ratio of ischemic stroke on atrial fibrillation was 1.05 (95% confidence interval [CI], 1.02 to 1.07; P = 1.3 × 10−5). One-unit increase of genetically determined ischemic stroke was associated with 0.02 (DBP: 95% CI, 0.001 to 0.034, P = 0.029; SBP: 95% CI, 0.006 to 0.034, P = 0.003) upper systolic and diastolic blood pressure levels. Higher genetically determined systolic and diastolic blood pressure levels were associated with higher atrial fibrillation risk (DBP: RR, 1.18; 95% CI, 1.03 to 1.35; P = 0.012. SBP: RR, 1.18; 95% CI, 1.01 to 1.38; P = 0.04). Specially, we also found the bidirectional causality between blood pressure and ischemic stroke. Conclusions: Our study provided a strong evidence that raised blood pressure in stroke patients increases the risk of atrial fibrillation and active acute blood pressure lowering can improve the outcome in ischemic stroke patients. [ABSTRACT FROM AUTHOR]