RESUMO
Gastrointestinal disturbances are commonly reported in children with autism, complicate clinical management, and may contribute to behavioral impairment. Reports of deficiencies in disaccharidase enzymatic activity and of beneficial responses to probiotic and dietary therapies led us to survey gene expression and the mucoepithelial microbiota in intestinal biopsies from children with autism and gastrointestinal disease and children with gastrointestinal disease alone. Ileal transcripts encoding disaccharidases and hexose transporters were deficient in children with autism, indicating impairment of the primary pathway for carbohydrate digestion and transport in enterocytes. Deficient expression of these enzymes and transporters was associated with expression of the intestinal transcription factor, CDX2. Metagenomic analysis of intestinal bacteria revealed compositional dysbiosis manifest as decreases in Bacteroidetes, increases in the ratio of Firmicutes to Bacteroidetes, and increases in Betaproteobacteria. Expression levels of disaccharidases and transporters were associated with the abundance of affected bacterial phylotypes. These results indicate a relationship between human intestinal gene expression and bacterial community structure and may provide insights into the pathophysiology of gastrointestinal disturbances in children with autism.
Assuntos
Transtorno Autístico/metabolismo , Transtorno Autístico/microbiologia , Metabolismo dos Carboidratos , Digestão , Gastroenteropatias/metabolismo , Gastroenteropatias/microbiologia , Mucosa Intestinal/microbiologia , Transtorno Autístico/complicações , Transtorno Autístico/fisiopatologia , Transporte Biológico/genética , Metabolismo dos Carboidratos/genética , Pré-Escolar , Clostridium/genética , Clostridium/isolamento & purificação , Clostridium/fisiologia , Comorbidade , Digestão/genética , Feminino , Hipersensibilidade Alimentar/complicações , Hipersensibilidade Alimentar/genética , Hipersensibilidade Alimentar/microbiologia , Gastroenteropatias/complicações , Gastroenteropatias/fisiopatologia , Humanos , Íleo/metabolismo , Lactente , Mucosa Intestinal/metabolismo , Mucosa Intestinal/patologia , Mucosa Intestinal/fisiopatologia , Masculino , Proteínas de Membrana Transportadoras/genética , Metagenômica , RNA Bacteriano/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , RNA Ribossômico 16S/genética , Fatores de Tempo , TranscriptomaRESUMO
SUMMARY: Analysis of multiple traits can provide additional information beyond analysis of a single trait, allowing better understanding of the underlying genetic mechanism of a common disease. To accommodate multiple traits in familial correlation analysis adjusting for confounders, we develop a regression model for canonical correlation parameters and propose joint modeling along with mean and scale parameters. The proposed method is more powerful than the regression method modeling pairwise correlations because it captures familial aggregation manifested in multiple traits through maximum canonical correlation.
Assuntos
Doença de Alzheimer/etnologia , Doença de Alzheimer/genética , Predisposição Genética para Doença/etnologia , Predisposição Genética para Doença/genética , Transtornos da Memória/etnologia , Transtornos da Memória/genética , Modelos Genéticos , Testes Neuropsicológicos , Doença de Alzheimer/diagnóstico , Simulação por Computador , Família , Hispânico ou Latino/estatística & dados numéricos , Humanos , Transtornos da Memória/diagnóstico , Estatística como AssuntoRESUMO
Matched case-control data analysis is often challenged by a missing covariate problem, the mishandling of which could cause bias or inefficiency. Satten and Carroll (2000, Biometrics56, 384-388) and other authors have proposed methods to handle missing covariates when the probability of missingness depends on the observed data, i.e., when data are missing at random. In this article, we propose a conditional likelihood method to handle the case when the probability of missingness depends on the unobserved covariate, i.e., when data are nonignorably missing. When the missing covariate is binary, the proposed method can be implemented using standard software. Using the Northern Manhattan Stroke Study data, we illustrate the method and discuss how sensitivity analysis can be conducted.
