Influence of Adiposity and Other Factors on the Gut Microbiota Composition

Overview

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.

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.