Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05664321 |
| Other study ID # |
KSU_Microbiota |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2019 |
| Est. completion date |
December 1, 2021 |
Study information
| Verified date |
December 2022 |
| Source |
King Saud University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
People who are overweight are getting more and more common in every region of the world.
However, despite significant progress being made in the treatment options available for
overweight, the worldwide incidence of overweight has not gone down, and the challenge of
overweight has become a worrisome phenomenon of our times. Additionally, the process that
underlie this illness and the etiological variables are not fully comprehended. As a result,
it is absolutely necessary to determine the factors that contribute to obesity and define the
responsibilities that each play. Researchers have devoted a significant portion of the better
part of the last decade to studying the microbiota of the gut to determine whether or not it
may play a factor in the development of obesity. Across spite of this, there is a paucity of
accessible epidemiological data in Saudi Arabia. In addition, the relationship between the
composition of the "gut microbiota" and obesity indices in youthful women of reproductive age
is little understood. In view of this, we decided to conduct a case study utilizing
whole-genome shotgun sequencing to compare the gut microbiota of obese women from Saudi
Arabia with that of healthy control participants. Our findings shed light on the significance
of the gut microbiota in obesity and provide useful insight into the creation of a method for
the therapy of obesity by means of microbiota transfer of fecal, antibiotics, probiotics, and
prebiotics. In addition, these data reveal prospective targets for guiding the selection of
probiotic strains for the needed gut microbiota regulation in the obesity therapy.
Description:
Materials and Methods
Study Design
From January 2019 to March 2020, a bigger study was carried out in the College of Applied
Medical Sciences clinic located on the campus of King Saud University in Riyadh. This
research will be a component of that larger study. The primary purpose of this research is to
investigate the nature of the connection that exists between the microbiota in the stomach
and obesity markers. The study's methodology is described in detail elsewhere. The sample
size of 92 was determined in a way similar to a prior study, with the significance level set
at 5% and the power set at 80%, based on the composition of the gut microbiota and its
differences among Saudi women in higher education. With a 95% Confidence Interval (CI) and
80% power, we expect a Firmicutes: Bacteroidetes of 0.9:0.4 in women of normal weight and
1.7:1.7 in women with obesity. Also, people were enlisted at random using fliers, faculty
aid, presentations, and social media. Participants with the following exclusionary criteria
were not included in the analysis: age 18 years, being overweight, body mass index (BMI)
25-29.9 kg/m2), being pregnant, following a specific diet (e.g., a calorie-restricted diet),
reporting the presence of gastrointestinal diseases in the past eight weeks, a history of
endocrine or oncological disease, psychiatric disorders, anorexia, other medical conditions,
or using multivitamins, vitamin B.
Each participant was given a time and date to return a stool sample to the nutrition clinic
in the same College where the study was conducted, along with a container in which to store
it until that time. Participants provided fasting blood samples and had their demographic,
nutritional, and anthropometric information recorded. Ninety-two Saudi female students
between the ages of 18 and 25 made up the final sample, and they were divided into two
groups: those with obesity (BMI 30 kg/m2) (n=44) and those without (BMI=18.50-24.99 kg/m2)
(n=48). The King Khalid University Hospital Institutional Review Board Committee of King Saud
University gave its clearance to the study's protocol (IRB #E-19-3625).
Anthropometric Measurements
Measurements of the subject's anthropometry were taken by qualified personnel utilizing
established protocols. The same measurements were taken twice, and the results from the
second recording were averaged for the final study. The subject's height and weight were
measured on an international standard scale and documented to the nearest 0.10 kg and 0.50
cm, respectively. The weight of the subject was determined and documented to the nearest 0.10
kg (Digital Pearson Scale; Oxford, USA). To calculate an individual's BMI, we took their
weight in kilograms and divided it by the square of their height in meters. The result was
the BMI. The World Health Organization (WHO) specified average weight as having a body mass
index (BMI) between 18.50 and 24.99 kilograms per square meter, while obese weight was
defined as having a BMI of more than 30 kilograms per square meter.
The waist of the individual was measured with a tape that did not stretch, and their hip
circumference was also measured with the same tape. The measurement of the hip circumference
was taken at the point on the body where the great trochanter was at its largest, whereas the
measurement of the waist circumference was taken at the point on the body where the lowest
rib and the umbilicus were at their narrowest. Both measurements were obtained to an accuracy
of within 0.50 centimeters. In order to calculate the waist-hip ratio (WHR), we first took
the average circumference of the waist and divided it by the average circumference of the
hips. The results were then sorted into the following categories: i) a normal WHR less than
0.83 and (ii) high ≥0.83 WHR. The bioelectrical impedance analysis (BIA) approach was
utilized in order to acquire information regarding one's body composition, namely their body
fat percentage (BF%) and their muscle mass (770 BIA; In body, Seoul, South Korea). The
categories are as follows: (i) normal ≤35%; and (ii) High >35% BF%.
Dietary Data
During the course of a guided interview, professional dietitians gathered dietary information
from participants. In order to evaluate the subjects' food intake, a validated version of the
Food Frequency Questionnaire (FFQ) was utilized by the Saudi Food and Drug Authority. The
participants were questioned about the amount of each food item that they consumed throughout
the course of the previous year.
Stool Analysis
Immediately following collection, each stool sample was immediately placed in containers that
could not leak air and had lids that fit securely before being frozen at -80 degrees Celsius.
After that, the materials were transported to the research facility, where they were kept in
a freezer at a temperature of -80 degrees Celsius until further examination. After that, the
DNA Kit was utilized in order to extract DNA from aliquots of 0.25 g of frozen feces
(Catalogue: 12830-50). We used a Nano-drop spectrophotometer to assess the concentration of
the extracted DNA as well as its purity (the ratio of 260 to 280) (Thermo Fisher Scientific,
Massachusetts, USA).
It was determined that the DNA concentration was either greater than or equal to 1.60. In
order to construct libraries, each sample was first assigned a set of combinatory dual
indexes, and then it went through a total of 12 rounds of Polymerase Chain Reaction (PCR).
The whole genome metagenomic sequencing technique was used to identify the total bacterial
DNA as well as the DNA of Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria,
Verrucomicrobia, and Fusobacteria. This information was then sent to CosmosID in the United
States, in order to determine the composition of the gut microbiota at the level of the main
microbial phyla.
Bioinformatics Methods
It was described previously that the unassembled sequencing reads were directly evaluated by
the CosmosID bioinformatics platform (which is owned and operated by CosmosID Inc. in
Maryland). This was carried out in order to carry out an examination of the microbiome of
multiple kingdoms, profile the genes that are responsible for antibiotic resistance and
virulence, and measure the relative abundance of various organisms.
Alpha Diversity The abundance score matrices at the phylum, genus, species, and strain levels
were used to create alpha diversity plots in the CosmosID analysis. Vegan is a R package that
allows for the computation of Chao, Simpson, and Shannon alpha, three measures of diversity.
Using the R package, we conducted Wilcoxon Rank-Sum tests comparing the groups. The R package
was utilized to create plots with p-value overlay.
Beta Diversity Principal Coordinate Analyses (PCoA) were computed using phylum, genus,
species, and strain-level matrices for bacteria obtained from CosmosID to generate beta
diversity PCoAs. R's vegan package's function was used to determine the Bray-Curtis
dissimilarity between groups, while ape's PCoA function was used to construct PCoA tables.
Vegan's adonis2 function was used to generate Permutational multivariate analysis of variance
(PERMANOVA) tests for each distance matrix, and vegan's betadisper function was used to
calculate and compare beta dispersion using the ANOVA method. The R package was used to
generate the plots.
Statistical Analysis The normality of the quantitative variables was checked before the
analysis was performed. The variables' normality was checked by inspecting their histograms,
Q-Q plots, and/or assessing their skewness and kurtosis. For continuous variables and final
results, we employed the independent samples t-test. Those variables that did not follow a
normal distribution pattern were evaluated using nonparametric tests. The median intake of
each type of meat was used to classify people into high and low intake categories, allowing
us to isolate the effects of specific cuts of meat. In the non-obese group, white meat
consumption was categorized as high white meat (HWM), n=32 or low white meat (LWM), n=16
based on whether or not it was above or below 34 grams of white meat per 1,000 calories.
Low-weight individuals (n=16) and high-weight individuals (n=28) were identified based on
whether or not their intake was at or over the recommended level of 25 grams of protein per
1,000 calories.
The non-obese group was further divided into those with a high red meat (HRM), n=21 intake
and those with a low red meat intake (LRM), n=27 based on their red meat consumption. In the
obese population, high red meat consumption was defined as (HRM, n=14) and low red meat
consumption was defined as (LRM, n=30).
Meat (total and by kind) and intestinal flora were correlated using the Pearson correlation
coefficient. Before doing any parametric tests, every non-normal variable was transformed.
Estimated statistical significance was reported using a p-value of 0.05 and a 95% CI.
Further, the Benjamini-Hochberg critical value was calculated for each correlation at a false
discovery rate of 0.25. The International Business Machines (IBM) Statistical Package for the
Social Sciences (SPSS) Statistics version for Windows was used for the analysis (version 24;
IBM Corp., New York, USA).
This study used the CosmosID application to compare the gut microbiota of people who were
obese and those who were not obese, as well as those who were stratified by their body fat
percentage and waist-to-hip ratio, to determine whether there was a correlation between the
types and amounts of meat they ate and their obesity. Concentrating on variation within a
single sample, we employed the Shannon test to measure alpha diversity, which depicts the
distribution of species abundances in a particular sample as a number that depends on species
evenness and richness. Bray-Curtis was used to analyze the degree of similarity or
dissimilarity between samples for beta diversity.
