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Characteristics of the colonic microbiome in patients with different obesity phenotypes (the original article)

https://doi.org/10.36233/0372-9311-66

Abstract

Introduction. The concept of heterogeneity in obesity depending on the risk of developing cardiometabolic complications has garnered attention in recent decades, since not everyone with obesity goes on to develop metabolic dysfunction.

The aim of the work is to study specific characteristics of colonic microbial communities in patients with different obesity phenotypes and in healthy individuals by employing metagenomics methods.

Materials and methods. A total of 265 individuals (44 men and 221 women; mean age 47.1 ± 4.8 years) were enrolled in the study. They were further divided into clinical groups: Healthy normal-weight individuals (n = 129); patients with obesity (n = 136), including metabolically healthy obesity (n = 40) and metabolically unhealthy obesity (n = 55). Quantitative and qualitative assessment of the intestinal microbiome was based on metagenomic analysis. Fecal samples were used to isolate DNA and perform sequencing of the variable v3-v4 region of the 16S rRNA gene.

Results. The study revealed statistically significant (p < 0.05) differences between quantitative and qualitative variables in studied phylotypes of colonic microorganisms in healthy individuals without obesity and in patients with different obesity phenotypes.

Discussion. Patients with obesity had higher levels of Bacteroidetes, Proteobacteria and lower levels of Actinobacteria, Firmicutes, TM7 (Saccharibacteria), Fusobacteria, and more frequently detected phyla Tenericutes, Planctomycetes and Lentisphaerae compared to healthy individuals. Metabolically healthy obese patients had more rarely detected phylum Lentisphaerae in their colonic microbiome, increased numbers of Firmicutes and reduced numbers of Bacteroidetes compared to metabolically unhealthy obese patients.

Conclusion. The findings demonstrate alterations in the colonic microbiome in patients with different obesity phenotypes.

Background

The worldwide obesity prevalence has nearly tripled over the last 40 years. Obesity-related complications such as type 2 diabetes, dyslipidemia and arterial hypertension impair quality of life, reduce life expectancy and significantly increase health care costs [1]. However, some studies have found that obesity not always entails metabolic abnormalities and increased risk of cardiometabolic complications. In scientific literature, such phenotype of obesity is known as metabolically healthy obesity (MHO) [2]. Because of the lack of universally accepted criteria to identify MHO, its prevalence varies widely among studies — 3 to 57% of obese patients [1].

There are commonly known factors that can influence the etiology and pathogenesis of obesity, including eating habits, lifestyle, environment, genetic predisposition, etc. In the meantime, none of them can explain the rapidly increasing prevalence of obesity; therefore, researchers are gaining greater appreciation of other important contributory factors.

The role of the intestinal microbiome in the development of obesity has garnered a lot of attention from researchers [3]. Around 70% of microorganisms (MOs) inhabiting the human body reside in the colon where the bacterial cell density is estimated at 1011 to 1012 per 1 mL of the content. The number of microbial genes responsible for production of numerous gut metabolites exceeds 3 million. In the meantime, the human genome consists of approximately 23 thousand genes [4]. Therefore, in the context of the global obesity epidemic, it is of great interest to understand how exactly microbial metabolomes can alter the human metabolic profile [3]. In 2006, Turnbaugh et al. performed one of the first studies where they showed the relationship between the gut microbiota and weight gain [5]. Today, there have been offered different mechanisms, through which the intestinal microbiome can influence the metabolic homeostasis of a human. They include production of short-chain fatty acids, metabolic endotoxemia, fatty acid oxidation, involvement in lipogenesis, appetite regulation, etc. [6].

Although the gut microbiota had been studied for many years, one of the main challenges was associated with cultivation of a limited range of MOs. The innovative technology provided researchers with tools for phylogenetic identification and quantification of intestinal microbiome components through the analysis of nucleic acids. Most of these methods and techniques are based on extraction of DNA and amplification of the 16S ribosomal RNA (rRNA) gene. It has been found that dominant phyla in the intestinal microbiome are Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia, while the first 2 phyla represent 90% of the intestinal microbiome community [7]. There are data evidencing that increased levels of two phyla Firmicutes and Actinobacteria as well as decreased levels of Bacteroidetes and Verrucomicrobia are associated with obesity [8].

The aim of the study is to explore specific characteristics of colonic microbial communities in patients with different obesity phenotypes and in healthy individuals by using metagenomic analysis.

Materials and methods

The cohort cross-sectional study was conducted at Center for Molecular Health, which is a center for digital and translational biomedicine, Internal Medicine Department No. 3, the central research laboratory of the Rostov State Medical University and the Kazan (Volga Region) Federal University in 2018–2020. The research was approved by the local independent ethics committees of Pirogov National Research Medical University (minutes # 186 of 26/6/2019) and Rostov Medical University (minutes # 20/19 of 12/12/2019).

To minimize impacts of climatic conditions, dietary patterns and ethnic factors on the intestinal microbiome, the study enrollees included only those who lived in the same area (Rostov Region and Rostov-onDon) during summer. A total of 265 individuals, including 44 (16.6%) men and 221 (83.4%) women; mean age 47.1± 4.8 years, were examined for the purpose of the further study.

Inclusion criteria:

  • older than age 18 years;
  • no antibiotics, prebiotics and probiotics taken 3 months before the study;
  • signed informed consent for participation in the study.

Exclusion criteria:

  • serious medical conditions (chronic kidney disease, chronic liver disease, chronic heart failure);
  • any gastrointestinal diseases (including nonspecific ulcerative colitis, Crohn’s disease, irritable bowel syndrome);
  • any acute condition, depression, alcohol abuse, pregnancy.

The enrolled 265 individuals were further divided into two clinical groups: The 1st group was composed of subjects without obesity and metabolic disorders (the control group); the 2nd group included patients with obesity. Additional criteria were introduced for further stratification of the two groups.

Additional criteria for inclusion in the 1st group:

  • body mass index (BMI) —18.5–24.9 kg/m2;
  • absence of metabolic disorders (dyslipidemia, hyperglycemia, hyperuricemia);
  • absence of arterial hypertension.

Additional criteria for inclusion in the 2nd group:

  • BMI ≥ 30 kg/m2;
  • waist circumference (WC) for men > 102 cm, for women > 88 cm.

The 1st group was composed of 129 people: 15 (11.6%) men, 114 (88.3%) women; mean age 39.6 ± 4.2 years; mean BMI 20.8 ± 2.1 kg/m2, WC 74 ± 5.8 cm.

The 2nd group included 136 patients with obesity: 28 (20.6%) men, 108 (79.4%) women; mean age 54.6 ± 4.7 years; mean BMI 33.8 ± 3.36 kg/m2, WC 99.7 ± 7.3 cm.

The clinical and laboratory profile of the participants from the 1st and 2nd groups is given in Table 1.

Table 1. Clinical and laboratory profile of the participants

To identify different obesity phenotypes by using NCEP-ATP III (The National Cholesterol Education Program, Adult Treatment Panel III)1 criteria, patients from the 2nd group (Table 2) were divided into 2 subgroups:

  • subgroup 2a — patients with MHO;
  • subgroup 2b — patients with metabolically unhealthy obesity (MUO).

Table 2. The NCEP ATPIII criteria for assessment of the metabolic status of patients for the 2nd group


Note
. MHО — metabolic health оbesity.

The healthy metabolic profile was set at fewer than 3 characteristics listed above [1]. Subgroups 2a and 2b were comparable in terms of age, BMI and WC.

Subgroup 2a was composed of 40 patients: 6 (15%) men, 34 (85%) women; mean age 49.5 ± 5.1 years; mean BMI 33.95 kg/m2, WC 101.5 cm. Subgroup 2b included 55 patients: 11 (20%) men, 44 (80%) women; mean age 51.3± 3.6 years; mean BMI 33.6 kg/m2; WC 98.9 cm. The clinical and laboratory profile of patients from subgroups 2a and 2b is given in Table 3.

Table 3. Clinical and laboratory characteristics of patients 2a and 2b subgroups (M ± m)

All the participants were asked to submit their current complaints and past medical records; all of them were examined through general checkups and had their body measurements taken (weight, height, WC, BMI). The relationship between the dietary intake and the obesity metabolic status was assessed by using food frequency questionnaires and food records. BMI was calculated following WHO experts’ guidelines (2003). WC was measured using a measuring tape midpoint between the lower margin of the last palpable rib and the iliac crest. Blood pressure was measured using a manual monitor and standard Korotkoff method.

For the purpose of carbohydrate metabolism evaluation, all the participants had their fasting blood sugar and immunoreactive insulin levels checked; the insulin resistance index was calculated by the formula: fasting blood glucose (mmol/L) × fasting insulin (u/L)/22.5. The lipid metabolism was assessed by measuring total cholesterol, low and high density lipoprotein cholesterol, and serum triglycerides. Insulin levels were measured by a Magpix analyzer (BioRad) and the Milliplex: Human Adipokine Magnetic Bead Panel 2 kit.

The Hitachi U-2900 spectrophotometer and Olvex Diagnosticum reagent kits were used for biochemical assays. Fecal samples were collected following the guidelines [9]. The metagenomic analysis of the intestinal community was performed at the Interdisciplinary Center of Shared Facilities of the Kazan Federal University. The QIAamp DNA stool mini kit (Qiagen) was used to extract DNA from stool samples. The variable v3-v4 region of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. Sequences of 16S rRNA genes were analyzed using the QIIME v.1.9.1 software and the Greengenes v.13.8 reference OTUs pre-clustered at the 97% sequence identity threshold. The relative abundance of bacterial taxa in the total reads is shown as a percentage range (0-1) based on the number of mapped reads for each taxon. Shannon, Simpson, and Chao1 and phylogenetic diversity indices were used to measure the alpha-diversity of the bacterial community.

The R version RStudio v.3.2 software was used for statistical analysis. The variables were checked for normal distribution using the Shapiro-Wilk test. Mean squared deviations, the median and quartiles (25%, 75%), minimum and maximum values in the sample were used as descriptive statistics for quantitative variables. The means in the groups were compared using the Mann-Whitney test; frequencies (%) were compared with the help of Fisher’s exact test. The latter test was used together with the Holm correction for multiple comparisons for detection frequencies of phylotypes detected in the colon. The median abundancies of the studied phylotypes and MOs detected in the colon were compared using the Kruskal-Wallis test (pairwise comparisons based on the post-hoc Nemenyi test). Differences were recognized as statistically significant at p < 0.05.

Results

Six dominant MO phylotypes were detected in the intestinal microbiome of participants from the 1st and 2nd groups, namely: Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia and the phylum Unassigned (Other). The unassigned group was represented by sequences having no matches in the reference database; therefore, these can be unknown bacteria or sequencing artifacts. In addition to the above phyla, the control group and the group of obese patients showed the dominance of Tenericutes (81 and 93% respectively) and Cyanobacteria (76 and 82% respectively) by frequency of detection. Significant differences were identified for 3 phyla: Tenericutes (p = 0.007), Planctomycetes (p = 0.03), Lentisphaerae (p = 0.047) (Figure).


The frequency of detection of some MO phylotypes in feces of the participants.

*p < 0.05 as compared with the 1st group.

The comparative analysis of the quantitative variables of the 1st and 2nd groups helped identify significant (p < 0.05), though bidirectional differences for 7 phyla (Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Cyanobacteria, TM 7 (Saccharibacteria), Fusobacteria) (Table 4). For example, in the group of obese patients, 3 phyla (Bacteroidetes, Proteobacteria, Cyanobacteria) demonstrated an increase (p < 0.05) in their levels, while 4 phyla (Actinobacteria, Firmicutes, TM 7 (Saccharibacteria), Fusobacteria) showed decreased levels.

Table 4. Significant differences in quantitative variables for some MO phylotypes in the participants’ colon, Me [min; max]

The phylogenetic diversity indices as well as Shannon, Simpson, and Chao1 were calculated for assessment of the alpha-diversity (Table 5). Significant differences between the control group and the group of obese patients were identified for the phylogenetic diversity index and the Chao1 index, thus suggesting a reduction in the alpha-diversity in fecal samples from the obese patients. On the other hand, the Shannon index did not demonstrate any difference in the groups and was significantly higher compared to the previously published data for the comparable group of patients with carbohydrate metabolism disorders [10]. However, these values of the Shannon index are not extreme and can be found in research literature with reference to stool samples from healthy individuals [11][12].

Table 5. Indices of the MO phylogenetic diversity in the 1st and 2nd groups (M ± SD)

When analyzing the detection frequencies for the studied MO phylotypes in patients with MHO and MUO, significant differences were found only for the phylum Lentisphaerae, which was more rarely (p = 0.03) detected in subgroup 2b. The analysis of quantitative variable revealed significant (p = 0.03) differences, namely increased Bacteroidetes and decreased Firmicutes levels in subgroup 2b.

The detection frequencies for the studied MO phylotypes in subgroups 2a and 2b were compared with the frequencies demonstrated by the healthy individuals (the 1st group) (Table 6). The general tendency was identified, showing 100% presence of 5 phyla (Unassigned (Other), Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria) and absence of 4 phyla (Planctomycetes, WPS-2 (Eremiobacterota), Gemmatimonadetes and Acidobacteria) in the intestinal microbiome. The phyla Verrucomicrobia, Tenericutes, and Cyanobacteria were dominant in the 1st group, subgroups 2a and 2b in terms of detection frequency. In subgroup 2a, Tenericutes and Lentisphaerae were detected significantly more frequently (p = 0.002 and p = 0.0009 respectively) than in subgroup 2b and the 1st group.

Table 6. Comparison of detection frequencies for MO phylotypes in the participants of the 1st group, subgroups 2a and 2b, abs. (%)


Note
. Pairwise comparisons were performed by using Fisher’s exact test and the Holm correction for multiple comparisons; "–" – no variations for calculation of p.

General tendencies were also revealed by the analysis of quantitative variables in the studied groups. The quantitative levels of the phylum Unassigned (Other) were significantly increased, while the Actinobacteria levels were decreased in subgroups 2a and 2b compared to the 1st group. However, statistically significant differences in 4 other phyla were found only in subgroup 2b. For example, quantitative variables for Bacteroidetes, Proteobacteria, and Fusobacteria were significantly higher (p < 0.05) and for Firmicutes – lower than the comparable variables in subgroup 2a and the 1st group (Table 7).

Table 7. Significant differences in intestinal microbiome quantitative variables among the participants, Me [min; max]

Discussion

The analysis of the questionnaires and food records did not show any significant difference in the total consumption of energy and macronutrients in individuals with two obesity phenotypes, thus being consistent with the results of most of the other related studies [13]. However, literature data about the role of nutrition in development of the MHO phenotype are controversial [14]. The currently available clinical and experimental data suggest that changes in the colonic microbiome can be a potential pathogenetic factor for development of obesity and metabolic syndrome.

Studies employing animal models and obese individuals confirmed specific changes in the composition of the intestinal microbiome, though the obtained results are controversial. For example, some researchers detected decreasing numbers of Bacteroidetes and increasing numbers of Firmicutes in obesity [15][16]. Schwiertz et al., on the contrary, reported a significant increase in the number of Bacteroidetes in obese and overweight individuals [17]. Duncan et al. did not find any correlation between BMI and changes in the Firmicutes and Bacteroidetes ratio [18].

During our study, we found quantitative and qualitative changes in the intestinal microbiome in obese individuals compared to healthy individuals and between the patients with different obesity phenotypes. The comparative analysis of quantitative variables of the studied colonic MO phylotypes in healthy individuals and obese patients revealed bidirectional statistically significant differences for 7 phyla: The increase in the studied variables for Bacteroidetes, Proteobacteria, Cyanobacteria and the decrease for Actinobacteria, Firmicutes, TM7 (Saccharibacteria), Fusobacteria. Despite the significant differences in quantitative variables for the above phylotypes, no statistically significant differences in frequencies of their detection were found in the 1st and 2nd groups. At the same time, Tenericutes, Planctomycetes and Lentisphaerae were significantly more frequently (p < 0.05) detected in the group of obese patients.

The data of our study show that the frequency of detection of the phylum Cyanobacteria in the control group and in the group of obese patients was 76 and 82%, respectively. In the meantime, the literature data confirm that the phylum Cyanobacteria is present in insignificant amounts in human fecal samples. Most likely that during the study, chloroplasts of plants had been sequenced from the consumed food, as the study was performed during summer when vegetable food accounted for the largest percentage in the dietary intake [19].

To date, very few studies have addressed the role of the intestinal microbiome in MHO development. One of the experimental studies found that the intestinal microbiome in mice with obesity and type 2 diabetes, as compared to mice with MHO, was characterized by a 20% decrease in Firmicutes to the benefit of Bacteroidetes and stable frequency of occurrence of the phylum Actinobacteria [20]. In our study, the occurrence frequency for the studied MO phylotypes in the patients with MHO and MUO was different only for the phylum Lentisphaerae, the occurrence of which was statistically higher in patients with MHO. However, the analysis of quantitative variables of 24 studied phylotypes in the subgroups of patients with MHO and MUO demonstrated statistically significant differences (p = 0.03) for two of them, namely for Bacteroidetes and for Firmicutes, the levels of which were increased and decreased, respectively, in the subgroup of patients with MUO.

Thus, the colonic microbiome in healthy individuals demonstrates certain differences from the colonic microbiome in obesity and its different phenotypes. However, identification of microbial biomarkers for obesity and its phenotypes needs further studies, which would address not only MO phylotypes detected in the colon, but also generic and specific characteristics of their representatives.

Conclusions

1. 5 MO phylotypes (Unassigned/Other, Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria) were detected in 100% of healthy adult individuals and obese patients in their intestinal microbiome (based on the example of residents from Rostov-on-Don and Rostov Region); the phylum Verrucomicrobia was detected in 85 and 88% of the participants, respectively; the phylum Tenericutes was detected in 81 and 93%, respectively.

2. In the obese patients, the intestinal microbiome demonstrates significantly (p < 0.05) increased frequencies of detection of Tenericutes, Planctomycetes and Lentisphaerae as compared to the similar detection frequencies in the healthy participants.

3. In the obese patients, the intestinal microbiome demonstrates a statistically significant (p < 0.05) increase in the levels of Bacteroidetes, Proteobacteria and a decrease in the levels of Actinobacteria, Firmicutes, TM 7 (Saccharibacteria), Fusobacteria as compared to the levels of these phyla in the healthy participants.

4. The phylum Lentisphaerae is detected significantly more rarely (p= 0.03) in the intestinal microbiome of the patients with the MHO phenotype as compared with the patients with MUO.

5. The patients with the MUO phenotype demonstrate significantly (p < 0.05) higher levels of Bacteroidetes and lower levels of Firmicutes compared to the patients with MHO.

6. The comparative analysis of the detection frequencies for the studied phylotypes in patients with different obesity phenotypes and in healthy people demonstrated significant differences in levels of two phyla: Tenericutes (p = 0.002) and Lentisphaerae (p = 0.0009) only in the MHO patients, but not in the MUO patients.

7. In the MUO patients, the intestinal microbiome is characterized by significantly (p < 0.05) higher levels of Bacteroidetes, Proteobacteria, Fusobacteria and lower levels of Firmicutes as compared to the microbiome in the healthy individuals.

8. In the MHO patients, the intestinal microbiome is characterized by significantly (p < 0.05) higher levels of the unidentified phylum (Unassigned/Other) and lower levels (p < 0.05) — of Actinobacteria as compared to the microbiome in the healthy individuals.

1. NCEP ATPIII — The Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III), USA

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About the Authors

A. M. Gaponov
Institute of Digital and Translational Biomedicine, Center for Molecular Health; Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology
Russian Federation

Andrey M. Gaponov — Cand. Sci. (Med.), Head, Department of Infectious Immunology, Center of Digital and Translational Biomedicine, Center for Molecular Health; Head, Laboratory of Infectious Immunology, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology

Moscow



N. I. Volkova
Rostov State Medical University
Russian Federation

Natalia I. Volkova — D. Sci. (Med.), Professor, Vice-rector for scientific work, Head, Department of Internal Diseases N 3

Rostov-on-Don



L. A. Ganenko
Rostov State Medical University
Russian Federation

Liliya A. Ganenko — assistant, Department of Internal Diseases N 3

Rostov-on-Don



Yu. L. Naboka
Rostov State Medical University
Russian Federation

Yulia L. Naboka — D. Sci. (Med.), Professor, Head, Department of microbiology and virology N 1

Rostov-on-Don



M. I. Markelova
Kazan (Volga Region) Federal University
Russian Federation

Maria I. Markelova — junior researcher, Research laboratory “Omics technologies”, Institute of Fundamental Medicine and Biology

Kazan



M. N. Siniagina
Kazan (Volga Region) Federal University
Russian Federation

Maria N. Siniagina – junior researcher, Research laboratory “Omics technologies”, Institute of Fundamental Medicine and Biology

Kazan



A. M. Kharchenko
Kazan (Volga Region) Federal University
Russian Federation

Anastasia M. Kharchenko — junior researcher, Research laboratory “Omics technologies”, Institute of Fundamental Medicine and Biology

Kazan



D. R. Khusnutdinova
Kazan (Volga Region) Federal University
Russian Federation

Dilyara R. Khusnutdinova — junior researcher, Research laboratory “Omics technologies”, Institute of Fundamental Medicine and Biology

Kazan



S. A. Roumiantsev
Institute of Digital and Translational Biomedicine, Center for Molecular Health; N.I. Pirogov Russian National Research Medical University
Russian Federation

Sergey A. Roumiantsev — D. Sci. (Med.), Professor, Сorresponding Member of the Russian Academy of Sciences, director, Center for Molecular Health, Moscow, Russia; Head, Department of oncology and hematology and radiotherapy of the Pediatric faculty, N.I. Pirogov Russian National Research Medical University

Moscow



A. V. Tutelyan
Institute of Digital and Translational Biomedicine, Center for Molecular Health; Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology Central Research Institute of Epidemiology
Russian Federation

Aleksey V. Tutelyan — D. Sci. (Med.), Professor, Corresponding Member of the Russian Academy of Sciences, Deputy director, Center for Molecular Health; Head, Department of molecular immunology, infectology and pharmacotherapy and the Molecular imaging laboratory, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology; Head, Laboratory of infections associated with the provision of medical care, Central Research Institute of Epidemiology

Moscow



V. V. Makarov
Center for Strategic Planning and Management of Medical and Biological Health Risks
Russian Federation

Valentin V. Makarov — Cand. Sci. (Biol.), Head, Department of analysis and forecasting of medical and biological risks to human health

Moscow



S. M. Yudin
Center for Strategic Planning and Management of Medical and Biological Health Risks
Russian Federation

Sergey M. Yudin — D. Sci. (Med.), Professor, General Director

Moscow



A. V. Shestopalov
Institute of Digital and Translational Biomedicine, Center for Molecular Health; Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology N.I. Pirogov Russian National Research Medical University
Russian Federation

Alexander V. Shestopalov — D. Sci. (Med.), Professor, Deputy director, Center of Digital and Translational Biomedicine, Center for Molecular Health; Director, Department of postgraduate education, residency, postgraduate studies, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Head, Department of biochemistry and molecular biology, Medical faculty, N.I. Pirogov Russian National Research Medical University

Moscow



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