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

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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 1^st group was composed of subjects without obesity and metabolic disorders (the control group); the 2^nd group included patients with obesity. Additional criteria were introduced for further stratification of the two groups.

Additional criteria for inclusion in the 1^st 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 2^nd group:

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

The 1^st 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 2^nd 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 1^st and 2^nd groups is given in Table 1.

 

Table 1. Clinical and laboratory profile of the participants

Indicator	Group 1 (n = 129)	Group 2 (n = 136)	P
Men                                                                                         n (%)	15 (11,6)	28 (20,6)	0,6
Women                                                                                     n (%)	114 (88,3)	108 (79,4)	0,6
Аge, years                                                                                M ± m	39,6 ± 4,2	54,6 ± 4,7	0,03
BMI, kg/m2                                                                              Me [min; max]	20,8 [19; 23]	34 [31; 36]	0,02
Waist, cm                                                                                 Me [min; max]	74 [69; 75, 5]	100 [95; 103]	0,01
Systolic blood pressure, mm Hg                                               Me [min; max]	120,5 [90; 125]	135 [125; 145]	0,03
Diasystolic blood pressure, mm Hg                                           Me [min; max]	74,5 [60; 90]	85 [80; 90]	0,001
Fasting plasma glucose, mmol/l                                                Me [min; max]	3,96 [4, 05; 5, 1]	5,57 [5, 1; 6, 93]	0,0001
Oholesterol, mmol/l                                                                   Me [min; max]	4,5 [4, 1; 5, 0]	5,42 [4, 62; 6, 2]	0,6
Low-density lipoprotein cholesterol, mmol/l                                Me [min; max]	3,11 [2, 4; 3, 21]	3,19 [2, 6; 3, 64]	0,7
High-density lipoprotein cholesterol, mmol/l                               Me [min; max]	1,93 [1, 49; 2, 24]	1,23 [1, 11; 1, 39]	0,03
Triglyceride, mmol/l                                                                   Me [min; max]	0,79 [0, 57; 1, 13]	1,65 [1, 33; 2, 34]	0,001

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

Oriterion		Mean
Blood pressure, mm Hg	systolic	> 130
	diastolic	> 85
Triglyceride, mmol/l		> 1,7
High-density lipoprotein cholesterol, mmol/l	men	< 1,03
	female	< 1,29
Fasting plasma glucose, mmol/l		> 5,6
Waist, cm	men	102
	female	> 88
MHO criteria		<3 of the above indicators

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)

Indicator	Subgroup 2a (n = 40)	Subgroup 2b (n = 55)	P
Men	6 (15%)	11 (20%)	0,6
Women	34 (85%)	44 (80%)	0,6
Age	49,05 ± 5,1	51,3 ± 3,6	0,7
Body mass index, kg/m2	34 ± 3,98	33,6 ± 3,39	0,8
Waist, cm	102 ± 8,37	98,9 ± 7,63	0,1
Systolic blood pressure, mm Hg	116 ± 11,5	143 ± 10,1	<0,0001
Diasystolic blood pressure, mm Hg	74,4 ± 7,53	90,2 ± 7,7	<0,0001
Fasting plasma glucose, mmol/l	4,87 ± 0,5	7,72 ± 2,36	<0,0001
Immunoreactive insulin, pg/ml	470 ± 565	550 ± 439	0,1
Index of insulin resistance	10,3 ± 12,3	20,4 ± 20,5	0,0003
Cholesterol, mmol/l	5,28 ± 1,16	5,67 ± 1,37	0,1
Low-density lipoprotein cholesterol, mmol/l	3,28 ± 0,91	3,05 ± 1,33	0,3
High-density lipoprotein cholesterol, mmol/l	1,38 ± 0,29	1,27 ± 0,29	0,04
Triglyceride, mmol/l	1,25 ± 0,54	2,58 ± 1,14	<0,0001

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 1^st and 2^nd 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 comparative analysis of the quantitative variables of the 1^st and 2^nd 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]

Fhylotypes	Group 1 (n = 129)	Group 2 (n = 136)	P
Bacteroidetes	0,3 [0, 2; 0, 43]	0,38 [0, 3; 0, 47]	0,0001
Proteobacteria	0,014 [0, 0087; 0, 028]	0,025 [0, 013; 0, 052]	<0,0001
Cyanobacteria	0,00027 [0, 00014; 0, 0014]	0,00059 [0, 00021; 0, 002]	0,02
Actinobacteria	0,023 [0, 012; 0, 055]	0,0098 [0, 0054; 0, 021]	<0,0001
Firmicutes	0,59 [0, 48; 0, 68]	0,52 [0, 43; 0, 59]	<0,0001
TM7 (Saccharibacteria)	0,000069 [0, 000069; 0, 00014]	0,00013 [0, 00007; 0, 00021]	0,04
Fusobacteria	0,000074 [0, 000069; 0, 00021]	0,00028 [0, 000074; 0, 0012]	0,007

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 1^st and 2^nd groups (M ± SD)

Index	Group 1 (n = 129)	Group 2 (n = 136)	р
Phylogenetic diversity index	42,92 ± 7,45	40,30 ± 7,41	0,00111
Chao1 index	4114,3 ± 1282,0	3771,2 ± 1539,1	0,00705
Shannon index	7,73 ± 0,81	7,60 ± 0,94	0,09153
Simpson index	0,97 ± 0,02	0,97 ± 0,02	0,2184
Number of operational taxonomic units	1993,1 ± 549,67	1895,7 ± 706,28	0,06655

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 1^st 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 1^st 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 1^st group.

 

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

Fhylotypes	Group 1	Subgroup 2a	p1-2a	Subgroup 2b	p1-2b
Unassigned;Other	129 (100)	40 (100)	-	55 (100)	-
Actinobacteria	129 (100)	40 (100)	-	55 (100)	-
Bacteroidetes	129 (100)	40 (100)	-	55 (100)	-
Firmicutes	129 (100)	40 (100)	-	55 (100)	-
Proteobacteria	129 (100)	40 (100)	-	55 (100)	-
Verrucomicrobia	110 (85)	35 (88)	1	47 (85)	1
Tenericutes	104 (81)	40 (100)	002	50 (91)	0,1
Cyanobacteria	98 (76)	34 (85)	0,8	45 (82)	0,9
Lentisphaerae	73 (57)	35 (88)	0009	36 (65)	0,3
Euryarchaeota	55 (43)	20 (50)	0,9	20 (36)	0,9
Elusimicrobia	37 (29)	17 (42)	0,24	11 (20)	0,27
TM7 (Saccharibacteria)	37 (29)	9 (22)	0,54	7 (13)	0,07
Synergistetes	31 (24)	6 (15)	0,6	16 (29)	0,6
Fusobacteria	25 (19)	9 (22)	1	14 (25)	1
Bacteria;Other	8 (6)	3 (8)	1	6 (11)	1
Crenarchaeota	5 (4)	4 (10)	0,44	0	0,44
Chloroflexi	1 (1)	0	1	0	1
Parvarchaeota	1 (1)	0	1	0	1
WS3 (Latescibacteria)	1 (1)	0	1	0	1
Spirochaetes	0	1 (2)	0,7	1 (2)	0,7
Acidobacteria	0	0	-	1 (2)	0,6
Planctomycetes	0	0	-	0	-
WPS-2 (Eremiobacterota)	0	0	-	0	-
Gemmatimonadetes	0	0	-	0	-

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 1^st 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 1^st group (Table 7).

 

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

Phylotypes	Group 1	Subgroup 2a	p1-2a	Subgroup 2b	p1-2b
Unassigned;Other	0,021 [0, 014; 0, 028]	0,038 [0, 019; 0, 047]	<0,0001	0,028 [0, 017; 0, 041]	0,03
Actinobacteria	0,023 [0, 012; 0, 055]	0,013 [0, 0076; 0, 027]	0,009	0,011 [0, 0061; 0, 021]	<0,0001
Bacteroidetes	0,3 [0, 2; 0, 43]	0,35 [0, 26; 0, 42]	0,6	0,43 [0, 34; 0, 5]	<0,0001
Firmicutes	0,59 [0, 48; 0, 68]	0.56 [0.48; 0.59]	0,2	0,46 [0, 38; 0, 54]	<0,0001
Proteobacteria	0,014 [0, 0087; 0, 028]	0.019 [0.0092; 0.043	0,31	0,027 [0, 021; 0, 055]	<0,0001
Fusobacteria	0,000074 [0, 000069; 0, 00021]	0,00015 [0, 000072; 0, 00021]	0,7	0,00055 [0, 00014; 0, 0018]	0,01

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 1^st and 2^nd 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.

 

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

Email: fake@neicon.ru
ORCID iD: 0000-0002-3429-1294

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

Russian Federation

N. I. Volkova

Rostov State Medical University

Email: fake@neicon.ru
ORCID iD: 0000-0003-4874-7835

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

Rostov-on-Don

Russian Federation

L. A. Ganenko

Rostov State Medical University

Author for correspondence.
Email: ganenko.lilia@yandex.ru
ORCID iD: 0000-0002-3381-9894

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

Rostov-on-Don

Russian Federation

Yu. L. Naboka

Rostov State Medical University

Email: fake@neicon.ru
ORCID iD: 0000-0002-0937-4573

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

Rostov-on-Don

Russian Federation

M. I. Markelova

Kazan (Volga Region) Federal University

Email: fake@neicon.ru
ORCID iD: 0000-0001-7445-2091

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

Kazan

Russian Federation

M. N. Siniagina

Kazan (Volga Region) Federal University

Email: fake@neicon.ru
ORCID iD: 0000-0002-8138-9235

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

Kazan

Russian Federation

A. M. Kharchenko

Kazan (Volga Region) Federal University

Email: fake@neicon.ru
ORCID iD: 0000-0002-9491-1694

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

Kazan

Russian Federation

D. R. Khusnutdinova

Kazan (Volga Region) Federal University

Email: fake@neicon.ru
ORCID iD: 0000-0002-9982-9059

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

Kazan

Russian Federation

S. A. Roumiantsev

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

Email: fake@neicon.ru
ORCID iD: 0000-0002-7418-0222

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

Russian Federation

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

Email: fake@neicon.ru
ORCID iD: 0000-0002-2706-6689

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

Russian Federation

V. V. Makarov

Center for Strategic Planning and Management of Medical and Biological Health Risks

Email: fake@neicon.ru
ORCID iD: 0000-0001-9495-0266

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

Moscow

Russian Federation

S. M. Yudin

Center for Strategic Planning and Management of Medical and Biological Health Risks

Email: fake@neicon.ru
ORCID iD: 0000-0002-7942-8004

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

Moscow

Russian Federation

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

Email: fake@neicon.ru
ORCID iD: 0000-0002-1428-7706

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

Russian Federation

References

Supplementary files