A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

DNLC: differential network local consistency analysis.

BACKGROUND: The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. RESULTS: In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. CONCLUSIONS: The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

Network Marker Selection for Untargeted LC-MS Metabolomics Data.

Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.

Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

Intervention for Increasing Vitamin D Supplementation in a Deficient Rehabilitation Population: Outcomes of a Quality Improvement Initiative.

BACKGROUND: Vitamin D deficiency (VDD) is highly prevalent and increases the risk of osteoporosis, falls, and fractures. Patients in acute inpatient rehabilitation have several risk factors for VDD, the adverse effects of which may hinder long-term functional gain. OBJECTIVE: To evaluate the prevalence of and risk factors for VDD in patients admitted to acute inpatient rehabilitation and to evaluate the efficacy of a standardized vitamin D screening and supplementation protocol. DESIGN: Prospective cohort study as part of a quality improvement initiative. SETTING: An academic, freestanding acute inpatient rehabilitation hospital. PARTICIPANTS: Patients consecutively admitted over a 4-month period between November 2014 and February 2015 (n=128 pre-intervention and n=129 post-intervention). INTERVENTION: Universal screening of vitamin D level on admission followed by utilization of a standard supplementation protocol. MAIN OUTCOME MEASURES: Vitamin D insufficiency (VDI) and VDD prevalence along with screening, and supplementation rates. RESULTS: Preintervention, 10.2% of patients were screened for VDD, with 23.1% VDI and 46.2% VDD. Postintervention, 89.9% were screened, with 31.9% VDI and 47.4% VDD. 6.3% of all patients were supplemented on vitamin D preintervention compared to 53.5% postintervention. In multivariate analyses, the odds of VDD was significantly associated with African American race (OR 7.30, 95% CI, 1.56-34.20, P = .12) and age younger than 65 (OR 13.62 95% CI, 2.51-73.83, P = .002). Diagnoses in the "other neurologic" category were associated with decreased odds of VDD (OR 0.01, 95% CI, 0.001-0.193, P = .002). CONCLUSIONS: Given the high prevalence of VDD in an acute inpatient rehabilitation hospital, a routine screening and standardized supplementation protocol may improve quality of care. LEVEL OF EVIDENCE: III.

Plasma High-Resolution Metabolomics Differentiates Adults with Normal Weight Obesity from Lean Individuals.

OBJECTIVE: This study explored underlying metabolism-related dysfunction by examining metabolomic profiles in adults categorized as lean, as having normal weight obesity (NWO), or as having overweight/obesity. METHODS: Participants (N = 179) had fasting plasma analyzed by liquid chromatography and high-resolution mass spectrometry for high-resolution metabolomics. Body composition was assessed by dual-energy x-ray absorptiometry. NWO was defined as BMI < 25 and body fat > 30% for women and > 23% for men. Differentiating metabolomic features were determined by using linear regression models and likelihood ratio tests with false discovery rate correction. Mummichog was used for pathway and network analyses. RESULTS: A total of 222 metabolites significantly differed between the groups at a false discovery rate of q = 0.2. Linoleic acid, ß-alanine, histidine, and aspartate/asparagine metabolism pathways were significantly enriched (all P < 0.01) by metabolites that were similarly upregulated in the NWO and overweight/obesity groups compared with the lean group. A module analysis linked branched-chain amino acids and amino acid metabolites as elevated in the NWO and overweight/obesity groups compared with the lean group (all P < 0.05). CONCLUSIONS: Metabolomic profiles of individuals with NWO reflected similar metabolic disruption as those of individuals with overweight/obesity. High-resolution metabolomics may help identify people at risk for developing obesity-related disease, despite normal BMI.

Shorter Perceived Outpatient MRI Wait Times Associated With Higher Patient Satisfaction.

PURPOSE: The aim of this study was to assess differences in perceived versus actual wait times among patients undergoing outpatient MRI examinations and to correlate those times with patient satisfaction. METHODS: Over 15 weeks, 190 patients presenting for outpatient MR in a radiology department in which "patient experience" is one of the stated strategic priorities were asked to (1) estimate their wait times for various stages in the imaging process and (2) state their satisfaction with their imaging experience. Perceived times were compared with actual electronic time stamps. Perceived and actual times were compared and correlated with standardized satisfaction scores using Kendall τ correlation. RESULTS: The mean actual wait time between patient arrival and examination start was 53.4 ± 33.8 min, whereas patients perceived a mean wait time of 27.8 ± 23.1 min, a statistically significant underestimation of 25.6 min (P < .001). Both shorter actual and perceived wait times at all points during patient encounters were correlated with higher satisfaction scores (P < .001). CONCLUSIONS: Patients undergoing outpatient MR examinations in an environment designed to optimize patient experience underestimated wait times at all points during their encounters. Shorter perceived and actual wait times were both correlated with higher satisfaction scores. As satisfaction surveys play a larger role in an environment of metric transparency and value-based payments, better understanding of such factors will be increasingly important.

Determination of Normal Distribution of Distended Colon Volumes to Guide Performance of Colonic Imaging With Fluid Distention.

The purpose was to determine the normal distribution of distended colon volumes as a guide for rectal contrast material administration protocols. All computed tomography colonography studies performed at Emory University Hospital, Atlanta, Georgia, between January 2009 and January 2015, were reviewed retrospectively. In total, 85 subjects were included in the analysis (64% [54 of 85] female and 36% [31 of 85] male). Mean patient age was 65 years (range: 42-86y). Distended colon volumes were determined from colon length and transaxial diameter measurements made using a 3-dimensional workstation. Age, sex, race, height, weight, and body mass index were recorded. The normal distributions of distended colon volumes and lengths were determined. Correlations between colonic volume and colonic length, and demographic variables were assessed. Mean colon volume was 2.1L (range: 0.7-4.4L). Nearly, 17% of patients had a distended colonic volume of >3L. Mean colon length was 197cm (range: 118-285cm). A weak negative correlation was found between age and colonic volume (r = -0.221; P = 0.04). A weak positive correlation was found between body mass index and colonic length (r = 0.368; P = 0.007). Otherwise, no significant correlations were found for distended colonic volume or length and demographic variables. In conclusion, an average of approximately 2L of contrast material may be necessary to achieve full colonic opacification. This volume is larger than previously reported volumes (0.8-1.5L) for rectal contrast material administration protocols.