scholarly journals The Potential Link between Gut Microbiota and Serum TRAb in Chinese Patients with Severe and Active Graves’ Orbitopathy

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Ting-Ting Shi ◽  
Lin Hua ◽  
Hua Wang ◽  
Zhong Xin
Keyword(s):  
Gut Microbiota ◽  
Metabolic Network ◽  
Intestinal Microbiota ◽  
Latent Class ◽  
Latent Class Model ◽  
Chinese Patients ◽  
Rrna Gene ◽  
Class Model ◽  
Graves Orbitopathy ◽  
Microbial Effects

Background and Objective. A previous study reported alterations in the intestinal microbiota in patients with Graves’ orbitopathy (GO). Thyrotropin receptor autoantibody (TRAb) stimulates orbital and periorbital tissues and plays a pivotal role in the development of GO. However, the association between gut microbiota and TRAb in GO patients has still remained elusive. In this study, we explored the relationships between gut microbiota and GO-related traits, in which we applied a metabolic-network-driven analysis to identify GO trait-related modules and extracted significant operational taxonomic units (OTUs). Methods. In the present study, we profiled gut microbiota using 16S rRNA gene sequencing in 31 GO patients. We performed metabolic-network-driven analysis to investigate the association between gut microbiota and GO-related traits (e.g., TRAb, TGAb, and TPOAb) in the combination of microbial effects. Results. Applying microbiome network analysis of cooccurrence patterns and analysis of topological properties, we found that s_Prevotella_copri and f_Prevotellaceae showed a significant correlation with TRAb. In particular, we applied the latent class model to explore the association between gut microbiota and GO-related traits in the combination of microbial effects. It was revealed that the subjects involved in the latent class model with the higher abundance of s_Prevotella_copri and g_Bacteroides had a higher TRAb level. Conclusions. Our results revealed the potential relationships between gut microbiota and GO-related traits in the combination of microbial effects. This study may provide a new insight into the interaction between the intestinal microbiota and TRAb-associated immune responses in GO patients.

scholarly journals Customers’ Mode Choice Behaviors of Express Service Based on Latent Class Analysis and Logit Model

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lian Lian ◽  
Shuo Zhang ◽  
Zhong Wang ◽  
Kai Liu ◽  
Lihuan Cao
Keyword(s):  
Logit Model ◽  
Choice Behavior ◽  
Latent Class ◽  
Latent Class Model ◽  
Policy Research ◽  
Multinomial Logit Model ◽  
Class Model ◽  
Taste Heterogeneity ◽  
Using Data ◽  
Service Choice

As the parcel delivery service is booming in China, the competition among express companies intensifies. This paper employed multinomial logit model (MNL) and latent class model (LCM) to investigate customers’ express service choice behavior, using data from a SP survey. The attributes and attribute levels that matter most to express customers are identified. Meanwhile, the customers are divided into two segments (penny pincher segment and high-end segment) characterized by their taste heterogeneity. The results indicate that the LCM performs statistically better than MNL in our sample. Therefore, more attention should be paid to the taste heterogeneity, especially for further academic and policy research in freight choice behavior.

scholarly journals A Comparison of Mixture Modeling Approaches in Latent Class Models With External Variables Under Small Samples

2017 ◽  
Vol 78 (6) ◽  
pp. 925-951 ◽  
Author(s):  
Unkyung No ◽  
Sehee Hong
Keyword(s):  
Sample Size ◽  
Latent Class ◽  
Latent Class Model ◽  
Mixture Modeling ◽  
Small Sample ◽  
Outcome Variable ◽  
Parameter Estimates ◽  
Class Model ◽  
Modeling Approaches ◽  
Distal Outcome

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class model with three predictor variables and a latent class model with one distal outcome variable. For the simulation, data were generated under the conditions of different sample sizes (100, 200, 300, 500, 1,000), entropy (0.6, 0.7, 0.8, 0.9), and the variance of a distal outcome (homoscedasticity, heteroscedasticity). For evaluation criteria, parameter estimates bias, standard error bias, mean squared error, and coverage were used. Results demonstrate that the three-step approaches produced more stable and better estimations than the other approaches even with a small sample size of 100. This research differs from previous studies in the sense that various models were used to compare the approaches and smaller sample size conditions were used. Furthermore, the results supporting the superiority of the three-step approaches even in poorly manipulated conditions indicate the advantage of these approaches.

scholarly journals Clostridial Butyrate Biosynthesis Enzymes Are Significantly Depleted in the Gut Microbiota of Nonobese Diabetic Mice

mSphere ◽  
2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Alessandro Tanca ◽  
Antonio Palomba ◽  
Cristina Fraumene ◽  
Valeria Manghina ◽  
Michael Silverman ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Intestinal Microbiota ◽  
Time Window ◽  
High Resolution Mass Spectrometry ◽  
Nod Mice ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Diabetic Mice ◽  
Nonobese Diabetic ◽  
Nonobese Diabetic Mice

ABSTRACT Increasing evidence suggests that the intestinal microbiota is involved in the pathogenesis of type 1 diabetes (T1D). Here we sought to determine which gut microbial taxa and functions vary between nonobese diabetic (NOD) mice and genetically modified NOD mice protected from T1D (Eα16/NOD) at 10 weeks of age in the time window between insulitis development and T1D onset. The gut microbiota of NOD mice were investigated by analyzing stool samples with a metaproteogenomic approach, comprising both 16S rRNA gene sequencing and microbial proteome profiling through high-resolution mass spectrometry. A depletion of Firmicutes (particularly, several members of Lachnospiraceae) in the NOD gut microbiota was observed compared to the level in the Eα16/NOD mice microbiota. Moreover, the analysis of proteins actively produced by the gut microbiota revealed different profiles between NOD and Eα16/NOD mice, with the production of butyrate biosynthesis enzymes being significantly reduced in diabetic mice. Our results support a model for gut microbiota influence on T1D development involving bacterium-produced metabolites as butyrate. IMPORTANCE Alterations of the gut microbiota early in age have been hypothesized to impact T1D autoimmune pathogenesis. In the NOD mouse model, protection from T1D has been found to operate via modulation of the composition of the intestinal microbiota during a critical early window of ontogeny, although little is known about microbiota functions related to T1D development. Here, we show which gut microbial functions are specifically associated with protection from T1D in the time window between insulitis development and T1D onset. In particular, we describe that production of butyrate biosynthesis enzymes is significantly reduced in NOD mice, supporting the hypothesis that modulating the gut microbiota butyrate production may influence T1D development.

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