The antioxidant enzyme system helps protect against intense exercise-induced oxidative damage and is related to the physical status of athletes. Evidence suggests that intestinal microbiota may be an important environmental factor associated with host metabolism, physiology, and antioxidant endogenous defense. However, evidence of the effect of gut microbiota status on exercise performance and physical fatigue is limited. We investigated the association of intestinal bacteria and exercise performance in specific pathogen-free (SPF), germ-free (GF), and Bacteroides fragilis (BF) gnotobiotic mice. Endurance swimming time was longer for SPF and BF than GF mice, and the weight of liver, muscle, brown adipose, and epididymal fat pads was higher for SPF and BF than GF mice. The serum levels of glutathione peroxidase (GPx) and catalase were greater in SPF than GF mice. Serum superoxide dismutase activity was lower in BF than SPF and GF mice. In addition, hepatic GPx level was higher in SPF than GF and BF mice. Gut microbial status could be crucial for exercise performance and its potential action linked with the antioxidant enzyme system in athletes.

Nonalcoholic fatty liver disease (NAFLD) is a serious liver disorder associated with the accumulation of fat and inflammation. The objective of this study was to determine the gut microbiota composition that might influence the progression of NAFLD. Germ-free mice were inoculated with feces from patients with nonalcoholic steatohepatitis (NASH) or from healthy persons (HL) and then fed a standard diet (STD) or high-fat diet (HFD). We found that the epididymal fat weight, hepatic steatosis, multifocal necrosis, and inflammatory cell infiltration significantly increased in the NASH-HFD group. These findings were consistent with markedly elevated serum levels of alanine transaminase, aspartate transaminase, endotoxin, interleukin 6 (IL-6), monocyte chemotactic protein 1 (Mcp1), and hepatic triglycerides. In addition, the mRNA expression levels of Toll-like receptor 2 (Tlr2), Toll-like receptor 4 (Tlr4), tumor necrosis factor alpha (Tnf-α), Mcp1, and peroxisome proliferator-activated receptor gamma (Ppar-γ) significantly increased. Only abundant lipid accumulation and a few inflammatory reactions were observed in group HL-HFD. Relative abundance of Bacteroidetes and Firmicutes shifted in the HFD-fed mice. Furthermore, the relative abundance of Streptococcaceae was the highest in group NASH-HFD. Nevertheless, obesity-related Lactobacillaceae were significantly upregulated in HL-HFD mice. Our results revealed that the gut microbiota from NASH Patients aggravated hepatic steatosis and inflammation. These findings might partially explain the NAFLD progress distinctly was related to different compositions of gut microbiota.

Analysis of categorical outcomes in a longitudinal study has been an important statistical issue. Continuous outcome in a similar study design is commonly handled by the mixed effects model. The longitudinal binary or Poisson-like outcome analysis is often handled by the generalized estimation equation (GEE) method. Neither method is appropriate for analyzing a multinomial outcome in a longitudinal study, although the cross-sectional multinomial outcome is often analyzed by generalized linear models. One reason that these methods are not used is that the correlation structure of two multinomial variables can not be easily specified. In addition, methods that rely upon GEE or mixed effects models are unsuitable in instances when the focus of a longitudinal study is on the rate of moving from one category to another. In this research, a longitudinal model that has three categories in the outcome variable will be examined. A continuous-time Markov chain model will be used to examine the transition from one category to another. This model permits an unbalanced number of measurements collected on individuals and an uneven duration between pairs of consecutive measurements. In this study, the explicit expression for the transition probability is derived that provides an algebraic form of the likelihood function and hence allows the implementation of the maximum likelihood method. Using this approach, the instantaneous transition rate that is assumed to be a function of the linear combination of independent variables can be estimated. For a comparison between two groups, the odds ratios of occurrence at a particular category and their confidence intervals can be calculated. Empirical studies will be performed to compare the goodness of fit of the proposed method with other available methods. An example will also be used to demonstrate the application of this method.