Gut Microbiota Composition in Hispanic and Non-Hispanic Children.

Overview

The human gastrointestinal (GI) tract is filled with millions of bacteria that can affect our health. These bacteria are linked with our overall health including obesity risk. In the United States the Hispanic population is one of the ethnic groups at higher risk of developing obesity. In this study the team will investigate differences in the GI bacterial composition between Hispanic and Caucasian children, and potentially demonstrate a correlation between the composition of metagenome and a higher risk to develop obesity. This will be done by collecting stool samples and comparing the bacteria found in the stool of Hispanic children (with and without obesity) and Caucasian children (with and without obesity.

I. SPECIFIC AIMS There are a vast number of microbes in the human gastrointestinal tract (GI) including bacterial, fungal and protozoal microorganisms which all together make our microbiome. Over the last years evidence has shown that the GI microbiome is linked with our overall health including obesity risk. The GI microbiota can influence both sides of the energy balance that includes factors influencing energy utilization from the diet and factors that play a role in the regulation of energy expenditure and storage by influencing host genes (1, 2). Studies of demonstrated that overweight and obesity rates are among the highest for Hispanic children in the United States. It is estimated that in the US 47% of Hispanic children are overweight and 31% have obesity, compared to 35% and 21% respectively for Caucasian non-Hispanic children. Literature has shown environment factors are associated with childhood obesity in Hispanic children including: parental influences, screen time, physical activity behavior, socioeconomic status/food security, and sleep duration (3,4). Thus far there have been no studies having investigated if there are significant differences in the gut microbiome of Hispanic children; moreover, whether these differences (taxonomy or gene expression) puts them at higher risk to develop obesity compared other ethnic groups. The investigators predict there are significant GI microbiome differences between Caucasian non-Hispanic children and Hispanic Children. Aim: Confirm differences in microbiome between Caucasian non-Hispanic children (with and without obesity) and Hispanic children (with and without obesity) in this pilot study. Hypothesis: Hispanic children will have a lower fecal bacterial diversity compared to Caucasian non-Hispanic children that is associated with more overall adiposity and impaired glucose homeostasis determined by: A) Next generation metagenomic "whole genome/shotgun" sequencing of DNA from fecal samples of cohorts which includes Hispanic children with obesity, Hispanic children without obesity, Caucasian non-Hispanic children with obesity and Caucasian non-Hispanic children without obesity. B) Bioinformatics computational analysis comparing the four groups to determine beta diversity (relative taxonomic) abundance. II. BACKGROUND AND SIGNIFICANCE Obesity is a serious health risk in the United States. The prevalence of childhood obesity has increased over the last two decades. According the U.S. Department of health and Human Services, recent date showed that the prevalence of obesity higher among youth aged 6-11 year and adolescents aged 12-19 years compared with children aged 2-5 years of age. The Hispanic population is among the most affected ethnic groups in the United States. Approximately 47% of Hispanic children are overweight and 31% have obesity. Multiple factors have been found to play a role in the development of childhood obesity in Hispanic children, including: parental influences, screen time, physical activity behavior, socioeconomic status/food security, and sleep duration. Over the last few years there has been a research interest in understanding the development and pathogenesis of obesity. Recent studies have found that the gut microbiota plays in an important role in the triggering and the development on obesity. The GI microbiota can influence both sides of the energy balance that includes factors influencing energy utilization from the diet and factors that play a role in the regulation of energy expenditure and storage by influencing host genes. In a study by Hou, they evaluated the 16S rRNA gene, the enterotypes and quantity of gut microbiota among obese children and a healthy control cohort. In this study they found that the composition of the gut microbiota showed significant differences between obese children and healthy controls. The results indicated that the phyla of Firmicutes and Bacteroidetes were the predominant fecal microbiome in both cohorts but the relative abundance ratio of Firmicutes and Bacteroidetes (F/B) in the obese cohort was significantly higher than that in the healthy controls. Firmicutes are associated with genes involved in carbohydrate catabolism and is rich in obese individuals, while Bacteroidetes are linked with diminished body mass. At this time there have been no studies that have investigated if there are significant differences in the gut microbiome of Hispanic children; moreover, whether these differences (taxonomy or gene expression) puts them at higher risk to develop obesity compared other ethnic groups. The study team feels it is important to identify possible microbiome differences in the Hispanic population that may put them at a higher risk of developing childhood obesity. III. RESEARCH DESIGN AND METHODS INCLUDING STATISTICAL ANALYSIS Study Subjects/Experimental Design The investigators will recruit and enroll 4 cohorts including 6 Hispanic children with obesity, 6 Hispanic children without obesity, 6 Caucasian non-Hispanic children with obesity and 6 Caucasian non-Hispanic children without obesity. Obesity will be defined as a BMI \>95%. Enrollment age will be between 6-12 years of age. The investigators will obtain baseline characteristics at the time of enrollment, including anthropometric measures (weight, length, BMI), dietary history (including 24 hour recall diary and a food frequency questionnaire), and physical activity history (Physical activity questionnaire). The investigators will obtain medication history, including history of use antibiotics, steroids and probiotics. Dietary and Physical activity measurement At fecal collection, investigators will obtain dietary information for the patient using a 30-food item food frequency questionnaire and a 24-hour food recall diary. Diet records will be analyzed using the Nutrient Data System for Research software (Minneapolis, MN). The investigators will determine intake of macro- and micronutrient intake as well as patterns of dietary intake using the latent class (person-centered approach) as well as factor (data-based approach) analyses. The investigators will collect physical activity data on the patient using the Physical Activity Questionnaire for Older Children (PAQ-C) and will analyze with Spearman correlation coefficients. Clinical data management will be the responsibility of the principal investigator and GI fellow/co-investigator and will use a secure database, REDCap, through Nemours' REDCap system. Fecal Sample Collection With permission from the parent/guardian, stool collected in a sterile container will be brought within 12 hours of defecation from home or the outpatient clinic to a separate clinical laboratory where feces will be collected through use of FLOQswab brush x 3 (Copan Diagnostics, Murrieta, CA) The swabs will be pre-labeled with a de-identified code reflecting patient number, sample number, and date and the sample will be placed into dry ice then transferred to a -80 degrees Celsius freezer until retrieval by the research staff for transfer to CHOP Microbiome Center. Bacterial DNA Isolation DNA will be extracted from samples using the DNeasy PowerSoil kit using the manufacturer's instructions (Qiagen, Germantown, MD). Shotgun libraries will be generated from 1 ng of DNA using the NexteraXT kit (Illumina, San Diego, CA, USA). Libraries will be sequenced on an Illumina HiSeq 2500 using 2x125bp chemistry in High Output mode. Bioinformatics processing and statistical analysis Differences in weight gain, HgbA1c, physical activity associated with nutritional diet records or other outcomes will be assessed using t-test or Mann Whitney U test, adjusting for covariates. The relationship between nutrition records and outcomes will be further examined using mixed effects models, adjusting for the effect of key physical activity or dietary variables. FASTQ files will be analyzed using an established computational pipeline developed at Nemours.