Chen, Jun

The lack of reliable, noninvasive methods to diagnose early nonalcoholic steatohepatitis (NASH) is a major unmet need. We aimed to determine the diagnostic accuracy of three‐dimensional magnetic resonance elastography (3D‐MRE), with shear stiffness measured at 60 Hz, damping ratio at 40 Hz, and magnetic resonance imaging proton density fat fraction (MRI‐PDFF) in the detection of NASH in individuals undergoing bariatric surgery. Obese adults at risk for NASH were enrolled between 2015 and 2017 (prospective cohort, n = 88) and 2010 and 2013 (retrospective cohort, n = 87). The imaging protocol consisted of multifrequency 3D‐MRE (mf3D‐MRE) with shear waves delivered at different frequencies to explore parameters that best correlated with histologic NASH, and MRI‐PDFF to estimate steatosis. The prospective cohort was used to establish the optimal mf3D‐MRE technical parameters for NASH detection. The two cohorts were then combined to derive predictive models of NASH and disease activity by nonalcoholic fatty liver disease activity score (NAS) using the three imaging parameters that correlated with NASH. A total of 175 patients (median age 45, 81% women, and 81 [46%] with histologic NASH) were used for model derivation. From the complex shear modulus output generated by mf3D‐MRE, the damping ratio at 40 Hz and shear stiffness at 60 Hz best correlated with NASH. The fat fraction obtained from MRI‐PDFF correlated with steatosis ( P < 0.05 for all). These three parameters were fit into a logistic regression model that predicted NASH with cross‐validated area under the receiver operating characteristic curve (AUROC) = 0.73, sensitivity = 0.67, specificity = 0.80, positive predictive value = 0.73 and negative predictive value = 0.74, and disease activity by NAS with cross‐validated AUROC = 0.82. Conclusion: The mf3D‐MRE allows identification of imaging parameters that predict early NASH and disease activity. This imaging biomarker represents a promising alternative to liver biopsy for NASH diagnosis and monitoring. The results provide motivation for further studies in nonbariatric cohorts.

AbstractRecent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed. In this paper, we propose the correlated sequence kernel association test (CSKAT) to address such correlations using the linear mixed model. Specifically, random effects are used to account for the outcome correlations and a variance component test is used to examine the microbiome effect. Compared to existing genetic association tests for longitudinal and family samples, we implement a correction procedure to better calibrate the null distribution of the score test statistic to accommodate the small sample size nature of data collected from a typical microbiome study. Comprehensive simulation studies are conducted to demonstrate the validity and efficiency of our method, and we show that CSKAT achieves a higher power than existing methods while correctly controlling the Type I error rate. We also apply our method to a microbiome data set collected from a UK twin study to illustrate its potential usefulness. A free implementation of our method in R software is available at https://github.com/jchen1981/SSKAT.

BackgroundPreoperative prediction of tumor recurrence is important in the management of patients with hepatocellular carcinoma (HCC).PurposeTo investigate whether tumor stiffness derived by magnetic resonance elastography (MRE) could predict early recurrence of HCC after hepatic resection.Study TypeRetrospective.PopulationIn all, 99 patients with pathologically confirmed HCCs after surgical resection.Field Strength/Sequence3.0T; preoperative MRE with 60‐Hz mechanical vibrations using an active acoustic driver.AssessmentRegions of interest (ROIs) were manually drawn in the tumors to measure mean tumor stiffness. Surgical specimens were reviewed for histological grade, capsule, vascular invasion, and surgical margins. The early recurrence of HCC was defined as that occurring within 2 years after resection.Statistical TestsCox proportional hazard models were used to evaluate risk factors associated with the time to early recurrence.ResultsHCCs with recurrence had higher tumor stiffness, higher rate of advanced T stage, vascular invasion, lower rate of capsule formation, larger tumor size, higher aspartate aminotransferase (AST), and hepatitis B virus (HBV)‐DNA level and aspartate aminotransferase / alanine aminotransferase ratio (P = 0.031, 0.007, 0.01, <0.001, 0.015, 0.034, 0.01, and 0.014, respectively) than HCCs without recurrence. Vascular invasion (hazard ratio [HR] = 2.922; 95% confidence interval [CI]: [1.079, 7.914], P = 0.035) and mean tumor stiffness (HR = 1.163; 95% CI: [1.055, 1.282], P = 0.002) were risk factors associated with early recurrence. Each 1‐kPa increase in tumor stiffness was associated with a 16.3% increase in the risk for tumor recurrence.Data ConclusionThe mean stiffness of HCCs may be a useful, noninvasive, quantitative biomarker for the prediction of early HCC recurrence after hepatic resection.Level of Evidence: 4Technical Efficacy: Stage 5J. Magn. Reson. Imaging 2019;49:719–730.

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero inflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero inflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero inflation remain largely undeveloped. Here we propose GMPR - a simple but effective normalization method - for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.

The clinical utility of microbiome biomarkers depends on the reliable and reproducible nature of comparative results. Underappreciation of the variation associated with common demographic, health, and behavioral factors may confound associations of interest and generate false positives. Here, we present the Midwestern Reference Panel (MWRP), a resource for comparative gut microbiome studies conducted in the Midwestern United States. We analyzed the relationships between demographic and health behavior–related factors and the microbiota in this cohort, and estimated their effect sizes. Most variables investigated were associated with the gut microbiota. Specifically, body mass index (BMI), race, sex, and alcohol use were significantly associated with microbial β-diversity (P < 0.05, unweighted UniFrac). BMI, race and alcohol use were also significantly associated with microbial α-diversity (P < 0.05, species richness). Tobacco use showed a trend toward association with the microbiota (P < 0.1, unweighted UniFrac). The effect sizes of the associations, as quantified by adjusted R 2 values based on unweighted UniFrac distances, were small (< 1% for all variables), indicating that these factors explain only a small percentage of overall microbiota variability. Nevertheless, the significant associations between these variables and the gut microbiota suggest that they could still be potential confounders in comparative studies and that controlling for these variables in study design, which is the main objective of the MWRP, is important for increasing reproducibility in comparative microbiome studies.

Abstract Motivation Recent studies have revealed a complex interplay between environment, the human microbiome and health and disease. Mediation analysis of the human microbiome in these complex relationships could potentially provide insights into the role of the microbiome in the etiology of disease and, more importantly, lead to novel clinical interventions by modulating the microbiome. However, due to the high dimensionality, sparsity, non-normality and phylogenetic structure of microbiome data, none of the existing methods are suitable for testing such clinically important mediation effect. Results We propose a distance-based approach for testing the mediation effect of the human microbiome. In the framework, the nonlinear relationship between the human microbiome and independent/dependent variables is captured implicitly through the use of sample-wise ecological distances, and the phylogenetic tree information is conveniently incorporated by using phylogeny-based distance metrics. Multiple distance metrics are utilized to maximize the power to detect various types of mediation effect. Simulation studies demonstrate that our method has correct Type I error control, and is robust and powerful under various mediation models. Application to a real gut microbiome dataset revealed that the association between the dietary fiber intake and body mass index was mediated by the gut microbiome. Availability and implementation An R package ‘MedTest’ is freely available at https://github.com/jchen1981/MedTest. Supplementary information Supplementary data are available at Bioinformatics online.

AbstractMotivationOne objective of human microbiome studies is to identify differentially abundant microbes across biological conditions. Previous statistical methods focus on detecting the shift in the abundance and/or prevalence of the microbes and treat the dispersion (spread of the data) as a nuisance. These methods also assume that the dispersion is the same across conditions, an assumption which may not hold in presence of sample heterogeneity. Moreover, the widespread outliers in the microbiome sequencing data make existing parametric models not overly robust. Therefore, a robust and powerful method that allows covariate-dependent dispersion and addresses outliers is still needed for differential abundance analysis.ResultsWe introduce a novel test for differential distribution analysis of microbiome sequencing data by jointly testing the abundance, prevalence and dispersion. The test is built on a zero-inflated negative binomial regression model and winsorized count data to account for zero-inflation and outliers. Using simulated data and real microbiome sequencing datasets, we show that our test is robust across various biological conditions and overall more powerful than previous methods.Availability and implementationR package is available at https://github.com/jchen1981/MicrobiomeDDA.Supplementary informationSupplementary data are available at Bioinformatics online.