Microbial and immune markers of patientswith metabolic syndrome and cardiovascular diseases:perspectives for early diagnostics

Tamara MELESHKO 1, 2 *, Viktor PETROV 3, 4, Tetyana FALALYEYEVA 5,

Nazarii KOBYLIAK 6, Nadiya BOYKO 1, 2, 7

1RDE Center of Molecular Microbiology and Mucosal Immunology, Uzhhorod National University, Uzhhorod, Ukraine; 2Department of Clinical Diagnostics and Pharmacology, Uzhhorod National University, Uzhhorod, Ukraine; 3Department of Family

Medicine and Outpatient Care, Medical Faculty 2, Uzhhorod National University, Uzhhorod, Ukraine; 4Prevention LTD, Uzhhorod,

Ukraine; 5Educational and Scientific Center “Institute of Biology and Medicine,” Taras Shevchenko National University of Kyiv,

Kyiv, Ukraine; 6Department of Endocrinology, Bogomolets National Medical University, Kyiv, Ukraine; 7Ediens LLC, Uzhhorod,

Ukraine

*Corresponding author: Tamara Meleshko, RDE Center of Molecular Microbiology and Mucosal Immunology, Uzhhorod National University, 8800 Uzh-

horod, Ukraine. E-mail: meleshkotv@ukr.net

A B STRACT

BACKGROUND: Intestinal microbiota affects human’s metabolic and physiological processes and changes in the microbiome are associated

with the progression of metabolic disorders such as obesity, atherosclerosis, and others. Despite the recent emphasis on the importance of the

study of intestinal microbiota as a diagnostic and therapeutic target for metabolic syndrome (MS) and cardiovascular disease (CVD), such stud-

ies have not been conducted in Ukraine.

METHODS: In this study, three groups of patients were formed: group N. 1 included 30 patients with MS and type 2 diabetes; group N. 2-42

patients with CVDs; group N. 3-15 healthy individuals. Gut microbiota profiles were measured using classical microbiology techniques. Pa- rameters such as C-reactive protein, uric acid, triglycerides, glycosylated hemoglobin, and cholesterol were assayed using Cobas c 311 (F.

Hoffmann-La Roche SA, Basel, Switzerland; Hitachi, Tokyo, Japan) Switzerland. Immune parameters such as total antibodies to Helicobacter

pylori, total immunoglobulin A (IgA) in serum, secretory IgA (SigA) in coprofiltrate, tumor necrosis factor alpha (TNF-α), interleukin - 1β (IL-

1β), interleukin - 10 (IL-10), interleukin - 12 (IL-12) were measured using immunosorbent systems.

RESULTS: The most typical pattern of changes in the gut microbiota of patients with MS was a significant decrease in the content of Bifido- bacterium bifidum and an increase in the number of transient and conditionally pathogenic microbiota. Patients with high levels of glucose and

glycosylated hemoglobin (more than 7.4%) demonstrated excess population levels of Enterococcus faecalis [(2.5±0.17) × 108 CFU/g] and Clos-

tridium tertium [(1.0±0.5) × 108 CFU/g] and reduced level of Lactobacillus acidophilus [(2.2±0.05) × 107 CFU/g]. The key changes in the gut

microbiota of patients with CVD included: decrease in the total number of normal microbiota representatives: Lactobacillus spp., Escherichia

coli with normal enzymatic properties along with a significant increase in the number of lactose-negative strains of E. coli, E. faecalis, Staphy- lococcus aureus, Staphylococcus saprophyticus, Candida albicans, and Candida krusei.

CONCLUSIONS: The identified stable correlations between species and quantitative composition of intestinal microbiota, immune parameters

and levels of glycosylated hemoglobin, cholesterol, overweight, and impaired glucose tolerance became the basis for the proposed use of indica-

tors of gut microbiota and immune markers of nonspecific subclinical inflammation for early diagnosis of cardiovascular diseases and metabolic

syndrome. The results obtained during the study also can be used for development of targeted microbiota correction approaches for the treatment

and prevention of these diseases.

(Cite this article as: Meleshko T, Petrov V, Falalyeyeva T, Kobyliak N, Boyko N. Microbial and immune markers of patients with metabolic syn-

drome and cardiovascular diseases: perspectives for early diagnostics. Minerva Biotechnol Biomol Res 2021;33:109-16. DOI: 10.23736/S2724-

542X.21.02784-X)

Key words: Gastrointestinal microbiome; Metabolic syndrome; Cardiovascular diseases; Biomarkers.

Metabolic diseases are often accompanied by gut mi-

crobiota imbalance, inducing a low-grade inflamma-

tory response in the body by destroying the gut barrier,

producing insulin resistance through metabolites affecting

host metabolism and hormone release, and forming a vi-

cious circle that promotes continuous progress of meta-

bolic diseases.1, 2 Gut microbiota signatures seem to be

highly specific for each individual, resulting in large in-

terindividual variations that depend on both host genetics

and environmental factors.3, 4 A strong link between the

gut microbiome and another major player in metabolism

and dysmetabolism, the endocannabinoidome, is also

emerging.5

Accumulated evidence suggests that alterations in gut

microbial community could play a role in cardiovascular

disease (CVD).6 In 70% of cases, people with heart failure

and hypertension develop gastrointestinal tract pathology,

primarily a violation of the intestinal microbiota. Under

the normal conditions, the intestine receives up to 20% of

cardiac output. The mucous membrane and submucosal

layer account for its largest part. Digestive system involves

intensive metabolism. Thus, the need for oxygen increases

sharply due to the constant exchange of epithelium, part

of which peels off during each meal and the other part is

restored.7

The fact that gut microbiota shows certain plasticity,

particularly in response to a diet, also makes it possible

to develop intervention strategies promoting a healthy gut

ecosystem to reduce the disease risk.8-10 These findings

reveal a two-way communication between the gut micro-

biota and the host, which might be shifted by a diet and

might influence the risk of developing immune-mediated

disorders, particularly at the early stages of development.4

Despite the recent emphasis on the importance of the

study of intestinal microbiota as a diagnostic and therapeu-

tic target for metabolic syndrome (MS) and CVD, no such

studies have been conducted in Ukraine.11-15

Data on intestinal microbiota changes in MS and CVD

patients, as well as their relationship with immunological

parameters, can be used to develop approaches to targeted

microbiota correction for the treatment and prevention of

these diseases.

Materials and methods

Participants and study design

In this study three groups of patients were formed: group

N.1 included 30 patients with MS and type 2 diabetes;

group N.2-42 patients with CVDs; group N.3-15 healthy individuals (control group). Patients were selected at the

Transcarpathian Regional Clinical Hospital of A. Novak

and Uzhhorod City Polyclinic.

The following inclusion criteria were used to select

patients with CVDs: diagnosed coronary heart disease,

stroke, carotid artery stenosis;16 hyperlipidemia; and a

signed informed consent to participate in the study. Ex-

clusion criteria involved smoking, alcohol, or drug abuse;

pregnancy; unstable medical status; clinically significant

renal or liver disease, acute inflammatory diseases at the

time of examination or a history of cancer; significant life-

style changes, mainly dietary habits and physical activity

in the period shorter than 6 months.

Patients with MS, obesity and type 2 diabetes were

selected according to the criteria typical of these nosolo-

gies: 1) main feature: central (abdominal) type of obesity

– waist circumference of more than 88 cm in women and

more than 104 cm in men; 2) additional criteria: arterial

hypertension (blood pressure ≥140/90 mm Hg), increase

in triglycerides (≥1.7 mmol/L), reduction in high-density

lipoprotein cholesterol (HDL) (<1.0 mmol/L in men; <1.2

mmol/L in women), increase in low-density lipoproteins

cholesterol (LDL) (>3.0 mmol/L), fasting hyperglycemia

(fasting plasma glucose ≥6.1 mmol/L), and impaired glu-

cose tolerance (plasma glucose 2 hours after glucose load-

ing in the range of ≥7.8 and ≤11.1 mmol/L).17 The presence

of central obesity in a patient and two of the additional

criteria is the basis for diagnosing his/her metabolic syn-

drome. Exclusion criteria: severe and/or insulin-dependent

diabetes mellitus.

For both experimental groups, the relationship of differ-

ent disease signs with indicators of microbial and immune

status of patients was studied.

According to the conclusions of the Commission on

Biomedical Ethics (Protocol N. 6/1 from 26.05.2020),

all studies were performed in compliance with the basic

provisions of the Good Clinical Practice (GMP) (1996),

the Council of Europe Convention on Human Rights and

Biomedicine from April 4, 1997), the World Medical

Association Declaration of Helsinki on the ethical prin-

ciples of scientific medical research with human participa-

tion (1964-2013), the order of the Ministry of Health of

Ukraine N. 690, according to which a person is the object

of research. All patients gave informed consent to partici-

pate in the study.

Analysis of gut microbiota

In order to perform microbiological analysis of gut mi-

crobiota, 1 g of feces was collected from the patients and mixed with 1 mL of phosphate buffered saline (PBS). Ten-

fold serial dilution of samples was performed and plated

correspondingly on the typical selective growth media,

namely Anaerobic Blood Agar, Bile Esculin Agar, Strep-

tococcus Selective Agar, Bacteroides bile esculin agar,

MacConkey Agar, Blood agar, Nutrient agar, Mannitol

Salt Agar, Wilson-Blair agar, Sabouraud Dextrose Agar,

Lactobacillus MRS Agar, Bifidobacterium Agar (all the

above-mentioned growth media produced by HiMedia

Laboratories, Mumbai, India) as well as on chromogenic

ChromaticTM Detection (Liofilchem, Teramo, Italy). Iden-

tification of isolated microorganisms was performed using

biochemical test systems: ANAERO-23, ENTERO-24,

NEFERM-test, Candida-23, STAPHYtest 16, and STREP-

TOtest 24 (Erba Lachema s.r.o., Brno, Czech Republic).

Study of the biochemical and immunological parameters

Such parameters as C-reactive protein, uric acid, triglyc-

erides, glycosylated hemoglobin, and cholesterol were

assayed using Cobas c 311 (Roche/Hitachi) Switzerland.

Immune parameters including total antibodies to Helico-

bacter pylori, total immunoglobulin A (IgA) in serum,

secretory IgA (SIgA) in coprofiltrate, tumor necrosis fac-

tor alpha (TNF-α), interleukin-1β (IL-1β), interleukin-10 (IL-10), interleukin-12 (IL-12) were measured using im-

munosorbent systems Vector-Best (Novosibirsk, Russia);

results were read at a wavelength of 450 nm using a plate

immunosorbent assay BioTek Elx800.

Statistical analysis

For the mathematical analysis of the data, licensed soft-

ware SPSS 17.0 was used. Parametric results were report-

ed as means with SD. The normality of distribution was

determined using the Lilliefors criterion. Internal group

differences before and after the intervention were evaluat-

ed using the Wilcoxon rank-sum test. Differences between

the groups were evaluated using the Mann-Whitney U test.

P<0.05 was considered significant. Correlation relation-

ships were determined using the Pearson coefficient.

Results

The data obtained for the first group of patients are pre-

sented in Table I-III. The connection between the content

of typical representatives of the commensal microbiota

– Bifidobacterium bifidum – and the level of cholesterol

in the serum was established. Patients with abdominal

obesity, overweight, and high cholesterol (over 8.24±1.2mmol/L) were found to have B. bifidum of up to (4.7±0.38)

× 108 CFU/g. At the same time, there was a decrease in

the number of Lactobacillus acidophilus [(2.2±0.05) × 107

CFU/g] and lactose-positive Escherichia coli [(4.0±0.11)

× 105 CFU/g] in individuals with high content of glycosyl-

ated hemoglobin (>7.4±2.4%) in the serum.

Patients with impaired glucose tolerance, overweight,

and high levels of cholesterol and glycosylated hemo-

globin (Table II) had high levels of total IgA (31.9±4.1

mg/L) in the serum and TNF-α (7.96±0.4 pg/mL). There

was a decrease in the secretion of SIgA in the coprofiltrate

(9.38±1.6 mg/L).

For the first time, we found that immunological status

undergoes significant changes under diabetes mellitus 2

with a high level of glycemia (9 mmol/L) and glycosylated

hemoglobin (7.4±2.4%) (Table II). Firstly, the level of pro-

inflammatory cytokines TNF-α was sharply increased to

7.96±0.4 pg/mL and IL-1β to 32±1.1 pg/mL in the serum,

which corresponds to modern ideas on the inflammatory

theory in the pathogenesis of obesity and diabetes melli-

tus 2. Secondly, there was a sharp increase in total IgA

(31.9±4.1 mg/L) in the serum, which correlates (see be-

low) with the level of glycosylated hemoglobin (7.4±2.4%)

and TNF-α (7.96±0.4 pg/mL). Finally, disturbance of the

resident microbiota, especially the amount of E. coli up to

(4.0±0.11) × 105 CFU/g — leads to a decrease in secre-

tory SIgA in the intestine (8.04±2.1 mg/mL), which in turn

promoted an increase in IL-10 (36.9±1.9 pg/mL) and total

IgA (31.9±4.1 mg/L) levels in the blood.

An increase in the blood uric acid concentration (over

495±27 μmol/L) was also observed in patients with MS.

The amount of L. acidophilus was equal to (2.2±0.05)

× 107 CFU/g, the amount of C. tertium increased to

(1.0±0.05) × 108 CFU/g, the content of lactose-positive

strains of E. coli decreased [(4.0±0.11) × 105 CFU/g] due

to the growth of lactose-negative strains – up to (1.2±0.11)

× 106 CFU/g and strains of P. vulgaris up to (1.4±0.1) ×

104 CFU/g. As mentioned above, the number of E. faecalis in these patients increased to (2.5±0.17) × 108 CFU/g, and

the gut microbiota was characterized by increased popula-

tion levels of conditionally pathogenic enterobacteria and

a reduced number of lactose-positive strains of E. coli up

to (4.0±0.11) × 105 CFU/g.

The results of serum examination of all patients with MS

and obesity for the presence of total antibodies to H. pylori

showed that in almost all individuals it had elevated levels

and reached values of up to 8.87±1.4 PI.

Patients in this experimental group demonstrated an in-

crease in the total content of E. faecalis and Enterococcus

faecium to 109 CFU/g, while the total amount of L. aci-

dophilus, Lactobacillus salivarius decreased to 105 CFU/g.

There was also a change in the ratio of E. coli isolates

with normal and reduced enzymatic properties with the

latter dominating. The content of lactose-negative strains

of E. coli correlates with the number of lactobacilli. The

dominant conditionally pathogenic enterobacteria include

Klebsiella pneumoniae and P. vulgaris, as well as clostrid-

ia (C. tertium), the content of which reached 108 CFU/g.

All examined patients demonstrated an increase in the syn-

thesis of total serum IgA and a decrease in the synthesis

of SIgA in the coprofiltrate, an increase in the amount of

IL-12 in the serum and the appearance of elevated popu-

lation levels of C-reactive protein and total antibodies to

H. pylori.

To confirm the possibility of using these microbial and

immune indicators as biomarkers, we searched for correla-

tions between them and the classical (biochemical) signs

of the disease.

It has been found that in patients with high levels of gly-

cosylated hemoglobin there was an increase in the colon of

the total number of bacteria Lactobacillus spp. (Pearson’s

correlation coefficient r=0.540) and a significant decrease

in the number of lactose-positive E. coli (r=-0.869).

The high content of glycosylated hemoglobin was

also accompanied by a significant increase in serum to-

tal IgA levels (r=0.894), increased TNF-α concentration (r=0.643), decreased secretion of both sIgA in the coprofil-

trate (r=-0.869) and IL-10 in the serum (r=-0.764), which

corresponds to the inflammatory theory of MS (obesity

and type 2 diabetes).

An indicator of lipid metabolism, such as triglycerides,

closely correlates with the amount of E. faecalis in the mi-

crobial coenosis of the intestine (r=0.832). The same index

closely correlates with serum IL-12 levels (r=0.853) and

was inversely correlated with total serum IgA (r=-0.788)

and SIgA (r=-0.692).

Another indicator of lipid metabolism – cholesterol lev-

els – also correlates with E. faecalis in the gut microbiota

(r=0.650), as well as with bifidobacteria (r=0.852). This

indicator, like the previous one, was correlated with the

levels of IL-12 (r=0.636), and its inverse correlation with

IL-10 (r=-0.703) was observed. Correlation was also found

between the levels of IL-10 and the content of E. coli with

normal enzymatic properties (r=0.889).

Overweight and waist circumference, as a basic indica-

tor of abdominal adipose tissue distribution, directly corre-

late with increased (>104 CFU/g) amount of conditionally

pathogenic enterobacteria (r=0.852 and r=0.866, respec-

tively). The correlation of both above indicators with total

antibodies to H. pylori (r=0.567 and r=0.657) and inverse

correlation with IL-10 (r=-0.557 and r=-0.668) were also

revealed.

C-reactive protein inversely correlates with the total

amount of Lactobacillus spp. (r=-0.586), Enterococcus

spp. (r=-0.605), and E. coli lactose-negative (r=-0.667).

This indicator also correlates with IL-10 (r=0.581), SIgA

(r=0.540) in the coprofiltrate and was inversely corre-

lated with IL-12 (r=-0.597). A close inverse correlation

of this indicator with the level of uric acid was revealed

(r=-0.752). Hence, the latter correlates with those indi-

cators with which the C-reactive protein inversely corre-

lates, namely with the total amount of Lactobacillus spp. (r=0.583), Enterococcus spp. (r=0.554), E. coli lactose-

negative (r=0.658), and IL-12 (r=0.549). Conversely,

it was inversely correlated with IL-10 (r=-0.520), SIgA

(r=-0.663) in the coprofiltrate. Additionally, this indicator

inversely correlated with SIgA (r=-0.659) and closely cor-

related with total antibodies to H. pylori (r=0.712).

Summarizing the above about the relationship of pre-

vious indicators with IL-10 and IL-12, we should note

that the level of IL-10 was inversely correlated with the

amount of glycosylated hemoglobin (r=-0.764), cholester-

ol (r=-0.703), overweight (r=-0.557) and waist circumfer-

ence (r=-0.668), as well as uric acid levels (r=-0.520), and

correlated with C-reactive protein (r=-0.581). In addition,

we found a close correlation of IL-10 with the content of

lactose-positive E. coli (r=0.889) and secretory SIgA in

the coprofiltrate (r=0.730), as well as an inverse correla-

tion with total IgA in the serum (r=-0.505) and the number

of bifidobacteria (r=-0.546).

As for IL-12 secreted in the blood serum, this indica-

tor extremely closely correlated with the total content of

E. faecalis and E. faecium in the large intestine (r=0.999),

as well as with the amount of B. bifidum (r=0.830) and

triglycerides (r=0.853), cholesterol (r=0.636) and uric

acid (r=0.549). The same indicator was inversely corre-

lated with C. tertium (r=-0.624) and C-reactive protein

(r=-0.597).

We have also found the following correlations for the

key members of the gut microbiota: for the pair Bifidobac-

terium spp., Enterococcus spp. r=0.839, for Lactobacillus

spp., lactose-negative E. coli r=0.992, and for C. tertium,

E. faecalis and E. faecium r=-0.632.

In patients with CVDs, an increase in the number of

conditionally pathogenic enterobacteria was detected up

to (3.5±0.3) × 105 CFU/g, Staphylococcus spp. (3.0±0.1) ×

106 CFU/g, and Candida spp. (3.7±1.19) × 107 CFU/g. The

concentration of lactose-negative E. coli [(1.0±0.07) × 105 CFU/g] correlated with the level of uric acid (450±20.2

μmol/L) (Table IV, V). An increase in population levels of

P. vulgaris [(7.0±0.33) × 106 CFU/g] was accompanied by

a significant decrease in the synthesis of SIgA in the copro-

filtrate (5.33±1.2 mg/mL) while an increase in the amount

of Clostridium spp. [(1±0.1) × 108 CFU/g] corresponds to

an increase in TNF-α production (7.14±0.4 pg/mL).

Reduction in the number of commensal microorgan-

isms, such as L. acidophilus [(2.0±0.3) × 107 CFU/g] and

E. coli with normal enzymatic activity [(3.8±0.32) × 106

CFU/g], was associated with an increase in E. faecalis

[(1.07±0.17) × 107 CFU/g] and an increase in the level of

conditionally pathogenic enterobacteria [(3.5±0.3) × 105

CFU/g]. Lack of sufficient level of B. bifidum [(1.3±0.1)

× 108 CFU/g] in the gut microbiota leads to an increase

in population levels of Staphylococcus spp. [(3.0±0.1) ×

106 CFU/g] and Candida spp. [(3.7±0.19) × 107 CFU/g],

as well as to a statistically significant decrease in the total

number of E. coli [(1.0±0.07) × 107 CFU/g].

It was also found that under the high persistence of P.

vulgaris strains in the gut microbiota [(7.0±0.33) × 105

CFU/g], the number of commensal B. bifidum statistically

significantly reduced [(1.3±0.1) × 108 CFU/g].

According to our quantitative data (Table IV, V), the

key changes in the gut microbiota of patients with CVD

include reduction in the total number of representatives of

the normal microbiota: Lactobacillus spp. (L. acidophilus,

L. salivarius subsp. salicinius), E. coli with normal enzy-

matic properties along with a significant increase in the

number of lactose-negative strains of E. coli, E. faecalis,

S. aureus, S. saprophyticus, C. albicans, and C. krusei.

Exceptions involve bifidobacteria and C. tertium, the

content of which in the intestines of patients did not un-

dergo significant changes and they were in high concentra-

tions (up to 108-109 CFU/mL). A decrease in the synthesis

of SIgA in the coprofiltrate and a decrease in the produc-

tion of all cytokines in the serum were also found.In order to confirm the possibility of using these mi-

crobial and biochemical parameters as biomarkers, we

searched for correlation between them.

It was found that uric acid closely correlated with the to-

tal antibodies to H. pylori (r=0.815), amount of E. coli with

reduced fermentation (r=0.694), and the content of micro-

organisms of the genera Staphylococcus spp. (r=0.799)

and Candida spp. (r=0.767). Therefore, total antibodies

for H. pylori correlated with the level of uric acid and with

the total amounts of Staphylococcus spp. (r=0.994) and

Candida spp. (r=0.984) but were not so closely related to

the amount of E. coli (r=0.419).

The level of total IgA did not significantly correlated

with any of the measured biomarkers. The level of secre-

tory SIgA in the coprofiltrate significantly correlated only

with the amount of P. vulgaris (r=0.903) and the num-

ber of bifidobacteria (r=0.520). TNF-α was interrelated

with the levels of both pro-inflammatory IL-1β (r=0.953)

and IL-12 (r=0.958) and anti-inflammatory cytokines

IL-10 (r=0.923), as well as with the amount of C. tertium

(r=0.582) in the large intestine. Correlations were found

between the following pairs: the total number of Bifido-

bacterium spp. and P. vulgaris (r=0.663), Lactobacillus

spp. and Enterococcus spp. (r=0.501), Lactobacillus spp.

and lactose-positive E. coli (r=0.698), Lactobacillus spp.

and lactose-negative E. coli (r=0.510), Lactobacillus spp.

and conditionally pathogenic enterobacteria, represented

mainly by the species K. pneumoniae (r=0.794), Entero-

coccus spp. and lactose-positive E. coli (r=0.769), Entero-

coccus spp. and lactose-negative E. coli (r=0.997), lactose-

positive E. coli and lactose-negative E. coli (r=0.691),

Staphylococcus spp. and Candida spp. (r=0.990).

Discussion

As can be seen from Table I, the most typical pattern of

changes in the gut microbiota was a significant decrease in the content of B. bifidum and an increase in the number

of transient and conditionally pathogenic microbiota (E.

faecalis, E. coli, K. pneumoniae, P. vulgaris). It should be

noted that in patients with high levels of glucose (more

than 9 mmol/L) in the peripheral blood and, respective-

ly, high levels of glycosylated hemoglobin (more than

7.4%), there is an excess of population levels of E. faeca-

lis [(2.5±0.17) × 108 CFU/g] and C. tertium [(1.0±0.5) ×

108 CFU/g] and a reduction in L. acidophilus (2.2±0.05)

× 107 CFU/g] observed. This poses a certain danger at

least because E. faecalis is a recipient of plasmids of anti-

biotic resistance and, when using antibiotics, can acquire

the properties of nosocomial strains. The latter, in turn,

can cause poor reactivity of the body and long treatment

of patients with high blood glucose levels. E. coli with

reduced enzymatic activity and opportunistic pathogens

identified in this group of patients are more likely to be

indirectly associated with obesity and overweight pa-

tients. An increase in the number of these conditionally

pathogenic enterobacteria, especially E. coli with reduced

enzymatic activity, leads to the loss of one of the main

functions of the commensal microbiota, namely partici-

pation in the hydrolysis and fermentation of dietary fi-

ber, the breakdown of sugars. The interaction between

anaerobic representatives and E. coli is stable, because

metabolites secreted into the intestinal lumen by B. bifi-

dum create a unique pH and microecological conditions

for the activation of E. coli enzymes. If such a regulator is

lost due to a decrease in the number of B. bifidum and ex-

cessive glucose in the environment, E. coli loses its enzy-

matic potential, multiplies rapidly and can penetrate the

mesenteric lymph nodes, peritoneum, spleen, liver, and

even the pancreas. Therefore, in our opinion, one of the

first indicators of metabolic disorders, in particular glu-

cose and cholesterol metabolism, may be the appearance

of E. coli with reduced enzymatic activity. The number of

opportunistic enterobacteria was detected in high titers of

up to 107 CFU/g (Table I).

Such changes in microbial and immune homeostasis,

namely a decrease in the species diversity of gut microbi-

ota, predominance of conditionally pathogenic enterobac-

teria in the large intestine, decrease in the number of lacto-

bacilli to the limit of detection, and a significant decrease

in E. coli population levels are the early manifestations of

CVD associated with the first disorders of lipid metabo-

lism (Table I, II). These changes are usually irreversible.

Impaired metabolism of carbohydrates and lipids leads to

microbiota alteration, especially its “probiotic” compo-

nent. After that, a cascade of shifts in microbiological and immune intestinal homeostasis is started, namely a sharp

decrease in the level of secretory immunoglobulin SIgA

(5.33±1.2) in the coprofiltrate and an increase in the levels

of proinflammatory cytokines in the blood.

Therefore, along with the classic markers of early diag-

nosis of dyslipidemia, metabolic syndrome, and obesity,

generally, such markers as IgA total in the blood and SIgA

secreted in the coprofiltrate, as well as levels of such repre-

sentatives of gut microbiota as Bifidobacterium spp., Lac-

tobacillus, Enterococcus spp., the ratio of lactose +/lactose

- E. coli can be used. Bacteria P. vulgaris [(7.0±0.33) ×

105 CFU/g] should be considered as indicators of primary

disturbance of a body’s lipid-hydrocarbon balance.

Conclusions

In conclusion, the identified stable correlations between

species and quantitative composition of intestinal mi-

crobiota, immune parameters and levels of glycosylated

hemoglobin, cholesterol, overweight, and impaired glu-

cose tolerance became the basis for the proposed use of

indicators of intestinal microbiota and immune markers of

nonspecific subclinical inflammation for early diagnosis of

cardiovascular diseases and metabolic syndrome. The re-

sults obtained during the study also can be used for devel-

opment of targeted microbiota correction approaches for

the treatment and prevention of these diseases.

Table I.—Changes in gut microbiota of patients with metabolic syndrome.

N. Microorganism Reference range Patients, N=30 - CFU/g Control group, N.=15 - CFU/g

1 Bifidobacterium bifidum 108-1010 (4.7±0.11) × 108 (1±0.1) × 108

2 Lactobacillus acidophilus 106-107 (2.2±0.05) × 107 (1±0.1) × 107

3 Enterococcus faecalis 105-106 (2.5±0.17) × 108 (1±0.2) × 108

4 Escherichia coli (with typical enzymatic activity) 107-108 (4.0±0.11) × 105 (1.2±0.2) × 108

5 Escherichia coli (lactose-negative strains) 106-107 (1.2±0.1) × 106 0

6 Proteus vulgaris Less than 104 (1.4±0.1) × 104 0

7 Conditionally pathogenic enterobacteria Less than 104 (1.0±0.12) × 107 0

8 Staphylococcus spp. Less than 104 0 0

9 Candida spp. Less than 104 0 0

10 Clostridium tertium Less than 105 (1.0±0.05) × 108 (1±0.1) × 105

Table II.—Indicative immunological markers of patients with metabolic syndrome.

N. Parameter, units Reference range Patients, N.=30 Control group, N.=15

1 C-reactive protein, mg/mL Less than 6 5±1 4±0.1

2 Uric acid, μmol/L Men 202.3 - 416.5; women 142.8 - 339.2 429±13.4 231.1±9.9

3 Total antibodies to Helicobacter pylori, PI Less than 1.1 8.87±1.4 1.0±0.1

4 Total Ig A (in serum), mg/L 0.8-4.0 31.9±4.1 2.9±0.19

5 SIg A secretory in serum, mg/mL 1.69-5.47 34.14±4.2 3.8±0.17

6 SIg A secretory in coprofiltrate, mg/mL 23.2-63.5 9.38±1.6 43.7±4.9

7 TNF-α, pg/mL 0-6 7.96±0.4 0.91±0.1

8 IL-1β, pg/mL 0-3.9 32±1.1 8.4±1.2

9 IL-10, pg/mL 3.9-12.5 36.9±1.9 7.4±1.1

10 IL-12, pg/mL 6.3-400 8.7±1.3 9.51±1.3

Table III.—General clinical and some biochemical parameters of patients diagnosed with metabolic syndrome.

N. Parameter, units Reference range Patients, N.=30 Control group, N.=15

1 Glycosylated hemoglobin, % 4-6 7.4±2.4 4±0.9

2 Triglycerides, mmol/L 1.7 2.5±0.7 1.7±0.2

3 Cholesterol, mmol/L 3.6-7.8 8.24±1.2 4.5±0.6

4 Diabetes, + present /- absent - + -

5 Obesity, + present /- absent - + -

6 Weight, kg 105±4.5 78±0.1

7 Waist circumference, sm 111±7 98±0.1

Table IV.—Changes in the gut microbiota of patients with CVD.

N. Microorganism Reference range Patients, N.=42 - CFU/g Control group, N=15 - CFU/g

1 Bifidobacterium bifidum 108-1010 (1.3±0.1) × 108 (1±0.1) × 108

2 Lactobacillus acidophilus 106-107 (2.0±0.3) × 107 (1±0.1) × 107

3 Enterococcus faecalis 105-106 (1.07±0.17) × 107 (1±0.1) × 108

4 Escherichia coli (with typical enzymatic activity) 107-108 (3.8±0.32) × 106 (1.2±0.1) × 108

5 Escherichia coli (with atypical enzymatic activity) 106-107 (1.7±0.3) × 107 0

6 Escherichia coli (lactose-negative strains) 106-107 (1.0±0.07) × 107 0

7 Proteus vulgaris Less than 104 (7.0±0.33) × 105 0

8 Conditionally pathogenic enterobacteria Less than 104 (3.5±0.3) × 105 0

9 Staphylococcus spp. Less than 104 (3.0±0.1) × 106 0

10 Candida spp. Less than 104 (3.7±0.19) × 107 0

11 Clostridium tertium Less than 105 (1.0±0.1) × 108 (1±0.1) × 105

Table V.—Indicative immunological markers in patients with CVD.

N. Parameter, units Reference range Patients, N.=42 Control group, N.=15

1 C-reactive protein, mg/mL Less than 6 5±0.4 4±0.1

2 Uric acid, μmol/L Men 202.3-416.5; women 142.8-339.2 450±20.2 231.1±9.9

3 Total antibodies to Helicobacter pylori, PI Less than 1.1 9.4±1.7 1.0±0.1

4 Total Ig A (in serum), mg/L 0.8-4.0 36.8±4.0 2.9±0.19

5 SIg A secretory in coprofiltrate, mg/mL 23.2-63.5 5.33±1.2 43.7±4.9

6 TNF-α, pg/mL 0-6 7.14±0.4 0.91±0.02

7 IL-1β, pg/mL 0-3.9 42.4±1.8 8.4±1.2

8 IL-10, pg/mL 3.9-12.5-13 59.4±2.9 7.4±2.1

9 IL-12, pg/mL 6.3-400 40.20±7.5 9.51±1.3

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