A Bayesian, generalized frailty model for comet assays.

This paper proposes a flexible modeling approach for so-called comet assay data regularly encountered in preclinical research. While such data consist of non-Gaussian outcomes in a multilevel hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modeled using exponential family models. This is true not only for binary and count data, but also for, example, time-to-event outcomes. Two important reasons for extending this family are for (1) the possible occurrence of overdispersion, meaning that the variability in the data may not be adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called overdispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen, 2007 ). While both of these issues may occur simultaneously, models combining both are uncommon. Molenberghs et al. ( 2010 ) proposed a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. Here, we use this method to model data from a comet assay with a three-level hierarchical structure. Although a conjugate gamma random effect is used for the overdispersion random effect, both gamma and normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation. Our proposed method has an upper hand over the traditional analysis in that it (1) uses the appropriate distribution stipulated in the literature; (2) deals with the complete hierarchical nature; and (3) uses all information instead of summary measures. The fit of the model to the comet assay is compared against the background of more conventional model fits. Results indicate the toxicity of 1,2-dimethylhydrazine dihydrochloride at different dose levels (low, medium, and high).

Joint modeling of hierarchically clustered and overdispersed non-gaussian continuous outcomes for comet assay data.

Multivariate longitudinal or clustered data are commonly encountered in clinical trials and toxicological studies. Typically, there is no single standard endpoint to assess the toxicity or efficacy of the compound of interest, but co-primary endpoints are available to assess the toxic effects or the working of the compound. Modeling the responses jointly is thus appealing to draw overall inferences using all responses and to capture the association among the responses. Non-Gaussian outcomes are often modeled univariately using exponential family models. To accommodate both the overdispersion and hierarchical structure in the data, Molenberghs et al. A family of generalized linear models for repeated measures with normal and conjugate random effects. Statistical Science 2010; 25:325-347 proposed using two separate sets of random effects. This papers considers a model for multivariate data with hierarchically clustered and overdispersed non-Gaussian data. Gamma random effect for the over-dispersion and normal random effects for the clustering in the data are being used. The two outcomes are jointly analyzed by assuming that the normal random effects for both endpoints are correlated. The association structure between the response is analytically derived. The fit of the joint model to data from a so-called comet assay are compared with the univariate analysis of the two outcomes.

Impact of the synbiotic combination of Lactobacillus casei shirota and oligofructose-enriched inulin on the fecal volatile metabolite profile in healthy subjects.

SCOPE: Hypothesis-driven approaches have mainly focused on the quantification of SCFAs as mediators of beneficial effects of synbiotics. However, the emergence of metabolite profiling strategies allows to evaluate the colonic metabolism from a top-down approach. In the present study, we evaluated the impact of a synbiotic combination on fecal metabolite profiles. METHODS AND RESULTS: A synbiotic combination (Lactobacillus casei Shirota cells+oligofructose-enriched inulin) was evaluated in nine healthy volunteers. Before the start, during and after 4-wk treatment, fecal samples were obtained. GC-MS technology was applied to analyze the volatile metabolites. Application of a Type III test revealed that the metabolite profiles from the three conditions were significantly different. We identified three volatile organic compounds, acetate, dimethyl trisulfide and ethyl benzene, which were significantly affected. The acetate levels increased, whereas the dimethyl trisulfide levels decreased during and after the intervention. For ethyl benzene only an effect during the synbiotic intervention period was observed. CONCLUSION: We report a detailed analysis of the influence of L. casei Shirota combined with oligofructose-enriched inulin on fermentation metabolites. Our results indicated a stimulation of saccharolytic fermentation and, importantly, a reduction of potentially toxic protein fermentation metabolites dimethyl trisulfide and ethyl benzene attended these effects.