Bayesian Image Mediation Analysis

Mediation analysis aims to separate the indirect effect through mediators from the direct effect of the exposure on the outcome. It is challenging to perform mediation analysis with neuroimaging data which involves high dimensionality, complex spatial correlations, sparse activation patterns and relatively low signal-to-noise ratio. To address these issues, we develop a new spatially varying coefficient structural equation model for Bayesian Image Mediation Analysis (BIMA). We define spatially varying mediation effects within the potential outcome framework, employing the soft-thresholded Gaussian process prior for functional parameters. We establish the posterior consistency for spatially varying mediation effects along with selection consistency on important regions that contribute to the mediation effects. We develop an efficient posterior computation algorithm scalable to analysis of large-scale imaging data. Through extensive simulations, we show that BIMA can improve the estimation accuracy and computational efficiency for high-dimensional mediation analysis over the existing methods. We apply BIMA to analyze the behavioral and fMRI data in the Adolescent Brain Cognitive Development (ABCD) study with a focus on inferring the mediation effects of the parental education level on the children's general cognitive ability that are mediated through the working memory brain activities

Subtyping Chronic Kidney Disease Patients And Adiposity-Obesity Related Metabolomics Analyses: Findings From The Chronic Renal Insufficiency Cohort Study

Chronic kidney disease (CKD) is a heterogenous condition that is often complicated by multiple serious comorbidities that create a large disease burden. Concurrent with the high CKD prevalence is the epidemic of obesity which increases the risks of adverse outcomes among people with kidney dysfunction. However, due in part to patient heterogeneity, the complex relationship between obesity and CKD is not fully understood. We aim to systematically examine phenotypic heterogeneity in patients with CKD and to study CKD mechanisms related to obesity-adiposity by integrating rich clinical characteristics of patients with high-dimensional metabolomics data. 3939 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) Study with stage 2-4 CKD at baseline were included in this body of research. We conducted two parallel clustering analyses using the machine learning methods of consensus clustering. First, we examined the overall CKD heterogeneity using 72 markers of patients’ demographics, biomarkers, and commonly collected clinical characteristics. Second, we identified the adiposity-obesity-related (AOR) CKD subgroups using 22 markers of patients’ obesity attributes, adiposity parameters, and comorbidity profiles. Third, in a random subset of CRIC participants with metabolomics data, we investigated the metabolic signatures associated with AOR CKD subgroups and tested metabolites as potential mediators of the association between AOR CKD subgroups and various clinical endpoints using Aalen additive hazards models and Cox regression. Among our findings, we identified three distinct CKD subgroups from the overall clinical data, and a different set of three-level AOR CKD subgroups featured with distinct patient profiles of adiposity/obesity and diabetes. Both sets of CKD subgroups were significantly and independently associated with different rates of future clinical outcomes. The metabolomics and mediation analyses revealed numerous metabolites to be mediators of the relationship between AOR CKD subgroups and clinical endpoints. Among them, multiple lipids, nucleoside, and amino acid metabolites were identified as key markers. In summary, our work quantitatively characterized CKD patient heterogeneity, shed light on adiposity-obesity-related disease mechanisms at both phenotypic and molecular levels, and highlighted potential therapeutic targets as well as metabolomics pathways for disease management and treatment. Validation using longitudinal metabolomics data and/or independent cohorts are needed

High-dimensional mediation analysis in survival models.

Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation