Obesity Clinical Trial
Official title:
Comparison of the Gut Microbiota Composition in Caucasian Hispanic and Caucasian Non-Hispanic Children With and Without Obesity
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.
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