Liu, Lei

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

AbstractBackgroundAccurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs.ObjectiveDevelop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG.MethodsWe developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model).ResultsOf the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording.ConclusionAccurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

The microbiome plays an important role in human health by mediating the path from environmental exposures to health outcomes. The relative abundances of the high‐dimensional microbiome data have an unit‐sum restriction, rendering standard statistical methods in the Euclidean space invalid. To address this problem, we use the isometric log‐ratio transformations of the relative abundances as the mediator variables. To select significant mediators, we consider a closed testing‐based selection procedure with desirable confidence. Simulations are provided to verify the effectiveness of our method. As an illustrative example, we apply the proposed method to study the mediation effects of murine gut microbiome between subtherapeutic antibiotic treatment and body weight gain, and identify Coprobacillus and Adlercreutzia as two significant mediators.

Abstract Researchers hypothesized that telomere shortening facilitates carcinogenesis. Previous studies found inconsistent associations between blood leukocyte telomere length (LTL) and cancer. Epigenetic reprogramming of telomere maintenance mechanisms may help explain this inconsistency. We examined associations between DNA methylation in telomere-related genes (TRG) and cancer. We analyzed 475 participants providing 889 samples 1 to 3 times (median follow-up, 10.1 years) from 1999 to 2013 in the Normative Aging Study. All participants were cancer-free at each visit and blood leukocytes profiled using the Illumina 450K array. Of 121 participants who developed cancer, 34 had prostate cancer, 10 melanoma, 34 unknown skin malignancies, and 43 another cancer. We examined 2,651 CpGs from 80 TRGs and applied a combination of Cox and mixed models to identify CpGs prospectively associated with cancer (at FDR < 0.05). We also explored trajectories of DNA methylation, logistic regression stratified by time to diagnosis/censoring, and cross-sectional models of LTL at first blood draw. We identified 30 CpGs on 23 TRGs whose methylation was positively associated with cancer incidence (β = 1.0–6.93) and one protective CpG in MAD1L1 (β = −0.65), of which 87% were located in TRG promoters. Methylation trajectories of 21 CpGs increased in cancer cases relative to controls; at 4 to 8 years prediagnosis/censoring, 17 CpGs were positively associated with cancer. Three CpGs were cross-sectionally associated with LTL. TRG methylation may be a mechanism through which LTL dynamics reflect cancer risk. Future research should confirm these findings and explore potential mechanisms underlying these findings, including telomere maintenance and DNA repair dysfunction. Cancer Prev Res; 11(8); 511–22. ©2018 AACR.