Citations

INTRODUCTION: Reflux bile acids are believed to promote esophageal adenocarcinoma (EAC), but the role of systemic bile acids is unknown. This study aimed to assess associations between systemic bile acids and stages of Barrett's esophagus (BE) progression. METHODS: Subjects with and without BE were enrolled in this multicenter cross-sectional study. Targeted serum bile acid profiling was performed, and a subset of subjects completed a validated food frequency questionnaire. RNA sequencing was performed on BE or gastric cardia tissue to assess bile acid associations with gene expression. RESULTS: A total of 141 subjects were enrolled with serum bile acids profiled (49 non-BE; 92 BE: 44 no dysplasia, 25 indefinite/low grade dysplasia, 23 high-grade dysplasia/EAC). Lower Healthy Eating Index score, older age, higher body mass index, and no proton pump inhibitor use were associated with increased levels of multiple bile acids. Global bile acid pools were distinct between non-BE and stages of BE neoplasia ( P = 0.004). Increasing cholic acid was associated with high-grade dysplasia/EAC compared with non-BE, even after adjusting for EAC risk factors (adjusted odds ratio 2.03, 95% confidence interval 1.11–3.71) as was the combination of unconjugated primary bile acids (adjusted odds ratio 1.81, 95% confidence interval 1.04–3.13). High cholic acid levels were associated with tissue gene expression changes including increased DNA replication and reduced lymphocyte differentiation genes. DISCUSSION: Alterations in serum bile acids are independently associated with advanced neoplasia in BE and may contribute to neoplastic progression. Future studies should explore associated gut microbiome changes, proneoplastic effects of bile acids, and whether these bile acids, particularly cholic acid, represent potential biomarkers or viable therapeutic targets for advanced neoplasia in BE.

Abstract Background. —The reported operating characteristics of the plasma fibrin D-dimer level for the diagnosis of acute pulmonary embolism vary widely. Objective. —To determine the sensitivity, specificity, predictive value, and clinical utility of the D-dimer for the diagnosis of pulmonary embolism, and to describe the effect of D-dimer assay method (enzyme-linked immunosorbent assay [ELISA], latex agglutination, membrane ELISA) and discriminate level, patient location at onset, comorbid disease, duration and intensity of concurrent heparin administration, and duration of symptoms on these operating characteristics. Design. —Prospective laboratory investigation. Setting. —Community and tertiary care teaching hospital. Patients. —Consecutive patients with suspected acute pulmonary embolism referred for pulmonary angiography from April 1993 through March 1996. Measurements. —Baseline characteristics, the duration and intensity of heparin anticoagulation, the time interval between symptom onset and plasma D-dimer testing, pulmonary angiography, and the D-dimer level on the day of pulmonary angiography. Results. —Of 105 consenting patients, 33 (31%) had a positive pulmonary angiogram. The D-dimer sensitivity/negative predictive value for the ELISA, latex agglutination (American Bioproducts Co/Diagnostica Stago and Biopool International), and membrane ELISA were 100%/100%, 94%/94%, 100%/100%, and 97%/96%, respectively, at a discriminate level of 250 μg/L or less. The clinical utility, defined as the prevalence of a negative test, ranged from 17% to 33%. D-dimer sensitivity was unaffected by patient location at onset, comorbid disease, or heparin therapy but was inversely related to the duration of symptoms. Conclusions. —The sensitivity of the plasma fibrin D-dimer for the diagnosis of pulmonary embolism depends on the assay method, the assay-specific discriminate level, and the duration of symptoms. At the appropriate discriminate level, the plasma D-dimer is a sensitive but nonspecific test for the diagnosis of pulmonary embolism.

Abstract Summary Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. Availability and implementation MiRKAT is available on CRAN as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Summary We present MetaMarker, a pipeline for discovering metagenomic biomarkers from whole-metagenome sequencing samples. Different from existing methods, MetaMarker is based on a de novo approach that does not require mapping raw reads to a reference database. We applied MetaMarker on whole-metagenome sequencing of colorectal cancer (CRC) stool samples from France to discover CRC specific metagenomic biomarkers. We showed robustness of the discovered biomarkers by validating in independent samples from Hong Kong, Austria, Germany and Denmark. We further demonstrated these biomarkers could be used to build a machine learning classifier for CRC prediction. Availability and implementation MetaMarker is freely available at https://bitbucket.org/mkoohim/metamarker under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Summary: One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of ‘omics’ features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package ‘glmgraph’ that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available. Availability and implementation: ‘glmgraph’ is implemented in R and C++ Armadillo and publicly available under CRAN. Contact: chen.jun2@mayo.edu or hongzhe@upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.

OBJECTIVE The American Diabetes Association (ADA) has recommended that the fasting plasma glucose (FPG) level used to diagnose diabetes be changed from 7.8 mmol/l (the level recommended by the National Diabetes Data Group [NDDG] in 1979) to 7.0 mmol/l. We examined the impact of this change on rates of progression to overt diabetes from different levels of FPG. RESEARCH DESIGN AND METHODS Using the laboratory database of Mayo Clinic, we assembled a cohort of 8,098 nondiabetic Olmsted County residents 40 years of age or older on 1 July 1983. Subjects were followed for a median of 9 years. RESULTS Among 7,567 individuals with follow-up FPG data, 778 (10.3%) progressed to ADA diabetes and 513 (6.8%; P < 0.0001) progressed to NDDG diabetes. The risk of developing ADA diabetes was 7, 19, and 39% for individuals with initial FPG values in the ranges of <5.6, 5.6–6.0, and 6.1–6.9 mmol/l, respectively. For progression to NDDG diabetes, the respective risks were 3, 11, and 25%. A clear gradient of risk was observed within the “normal” range of FPG (<5.6 mmol/l). Among the 793 individuals who developed ADA diabetes, 222 (29%) developed NDDG diabetes simultaneously and 291 (37%) developed NDDG diabetes later. In all FPG subgroups, progression to ADA diabetes occurred ∼7 years sooner than progression to NDDG diabetes. CONCLUSIONS The baseline level of FPG is a major predictor of an individual's risk of developing diabetes. The proposed change in the diagnostic criteria for diabetes will lead to earlier diagnosis among individuals who are destined to develop the disease.