Influence of Adiposity and Other Factors on the Gut Microbiota Composition
Condition: Obesity; Metabolic Syndrome
MeSH terms: Metabolic Syndrome
Study type: Observational

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.
    

Detailed 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.
    

Intervention name(s): Observational, case-control study
Intervention type(s): Other
Intervention description(s): obese [(BMI ≥30 kg/m2), (n=44)], and non-obese [(BMI= 18.50-24.99 kg/m2), (n=48)].
Other intervention name(s): Obese and normal weight
Observational model: Case-Control
Time perspective: Cross-Sectional
Sampling method: Probability Sample
Study population: 
        92 Saudi female students aged (18 to 25 years), classified into; obese [(BMI ≥30 kg/m2),
        (n=44)], and non-obese [(BMI= 18.50-24.99 kg/m2), (n=48)].
      
Biospecimen description: 
      the DNA was extracted from 0.25 g frozen stool aliquots by the QIAGEN PowerFecal DNA Kit
      (Catalogue: 12830-50). The purity of the isolated DNA (260/280 ratio) and concentration was
      measured utilizing a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
    
Keywords: gut microbiota; diet; inflammatory markers