Front. Oral. HealthFrontiers in Oral HealthFront. Oral. Health2673-4842Frontiers Media S.A.10.3389/froh.2024.1488833Oral HealthOriginal ResearchPeriodontal pathogens and obesity in the context of cardiovascular risks across age groupsLeonovGeorgy1*VaraevaYurgita1LivantsovaElena1VasilyevAndrey23VladimirskayaOlga2KorotkovaTatyana1NikityukDmitry1StarodubovaAntonina14Department of Cardiovascular Pathology and Diet Therapy, Federal Research Centre for Nutrition, Biotechnology and Food Safety, Moscow, RussiaDepartment of Microbiology, Central Research Institute of Dental and Maxillofacial Surgery, Moscow, RussiaInstitute of Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow, RussiaTherapy Faculty, Pirogov Russian National Research Medical University, Moscow, Russia
Edited by: Paolo De Angelis, Catholic University of the Sacred Heart, Italy
Reviewed by: Xiaolei Li, University of Pennsylvania, United States
Edoardo Rella, Catholic University of the Sacred Heart, Italy
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Background
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity among noncommunicable diseases. Over the past decade, there has been a notable increase in the prevalence of CVDs among young individuals. Obesity, a well-known risk factor for CVDs, is also associated with various comorbidities that may contribute to cardiovascular risk. The relationship between periodontal pathogens and CVD risk factors, including obesity, smoking, lipid metabolism disorders, and inflammatory markers, remains underexplored.
Methods
This study examined the relationship between six periodontal pathogens (Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, Treponema denticola, Tannerella forsythia, Prevotella intermedia, and Fusobacterium nucleatum) and CVD risk factors among 189 subjects stratified by age and body mass index (BMI). Body composition was assessed via bioimpedance analysis, and blood samples were analyzed for lipid profiles, glucose, and proinflammatory cytokines. Oral samples were collected for polymerase chain reaction (PCR) analysis to identify periodontal pathogens. Cardiovascular and diabetes risk scores were calculated using the SCORE and FINDRISC scales.
Results
The prevalence of periodontal pathogens in the population was 33.0% for P. gingivalis, 47.8% for P. intermedia, 63.4% for A. actinomycetemcomitans, 46.6% for T. forsythia, 46.6% for T. denticola, and 89.2% for F. nucleatum. Significant age- and BMI-related differences were observed in pathogen prevalence, particularly with P. gingivalis, P. intermedia, and T. denticola. Young obese individuals exhibited a higher prevalence of P. intermedia and T. forsythia. P. gingivalis was found to be associated with hypertension and dyslipidemia, while P. intermedia was linked to hypertension and obesity. T. denticola was associated with obesity, dyslipidemia and smoking, whereas T. forsythia was linked to dyslipidemia alone.
Conclusions
This study highlights the potential connection between periodontal pathogens and risk factors associated with cardiovascular disease, including smoking, elevated BMI, increased adipose tissue, hypertension, and dyslipidemia. Further research is required to determine the causal relationships between oral microbiome dysbiosis, obesity and, systemic diseases and to develop an effective strategy for preventing oral health-related CVD risk factors in young adults.
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity among noncommunicable diseases, representing a significant global health challenge (1). In recent decades, the prevalence of CVDs has notably increased among individuals under 55 years of age, with a marked rise in cases of myocardial infarction and stroke within this demographic (2). A substantial body of evidence indicates that cumulative exposure to CVDs risk factors from childhood through young adulthood significantly contributes to this trend (3).
Obesity is a trigger for many diseases, such as non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, cardiovascular diseases, diabetes, and some types of cancer (4, 5). The relationship between adipose tissue and CVDs is mediated through both direct and indirect pathways associated with obesity-related comorbidities. For instance, obesity is a well-established risk factor for several traditional CVDs, such as atherogenic dyslipidemia, hypertension, and diabetes (6). Additionally, obesity-related obstructive sleep apnea can elevate the risk of CVDs through mechanisms involving hypoxia, cardiac arrhythmias, insulin resistance, and hypertension (7). There is some evidence of a connection between oral microorganisms, obesity and metabolic disorders, both at the level of overall diversity and individual species (8, 9).
The physiology and ecology of the microbiota are intimately linked to those of the host at both the macro and microscopic levels (10). The human oral microbiome, comprising bacteria, archaea, viruses, fungi, and protozoa, includes over 700 identified species of microorganisms (11). Oral bacteria primarily exist as structured communities of aggregated bacterial cells (biofilms) (12). Dysbiosis of the oral microbiota represents a complex, multifactorial displacement of native microorganisms within the oral cavity, where potentially pathogenic species supersede commensal flora (13–15). Opportunistic anaerobic bacteria involved in periodontal diseases (PDs) exert significant negative effects on systemic health. PDs are microbial-induced inflammatory and multifactorial chronic immunological diseases leading to damage to the gums, periodontal ligaments, and alveolar bone (16). At present, a multitude of periodontopathic organisms have been identified, with ongoing research elucidating their characteristics and pathogenic potential (17, 18). The most substantial evidence for negative impacts on systemic disorders, particularly cardiometabolic health, was observed in studies involving Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, Treponema denticola, Tannerella forsythia, Prevotella intermedia, and Fusobacterium nucleatum (19–21).
There are several potential reasons for the association of CVDs and periodontal diseases: systemic inflammation, the direct damaging effect of microorganisms and their metabolites entering the bloodstream, as well as alterations in the intestinal microbiome due to the transfer of oral bacteria (22). Systemic inflammation is a potential underlying mechanism of the association between oral diseases and increased risk of cardiovascular disease (23, 24). Elevated inflammatory markers, such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and interleukin-6 (IL-6), have been correlated with higher cardiovascular morbidity and mortality (25). According to some data, periodontitis-associated systemic inflammation can cause vascular dysfunction (26). The hematogenous route facilitates the spread of oral bacteria to distant organs, as the ulcerated epithelium of the periodontal pocket allows microorganisms and their toxins to enter the systemic circulation, leading to bacteremia (27). Recent research has shown that P. gingivalis, P. intermedia, A. actinomycetemcomitans, T. forsythensis, and T. denticola and others are present in 20%–70% of carotid atheromas (28). Cross-sectional studies have demonstrated a higher incidence of atherosclerotic complications in patients with periodontal disease. In the NHANES III cohort, severe periodontal disease was associated with an almost 4-fold higher incidence of myocardial infarction than in patients without periodontal disease (29). Moreover, a study involving 52,677 hypertensive participants indicated that dental caries is linked to an elevated CVD (30). Oral microorganisms may serve as a new biomarker for CVD and metabolic disorders. Interdisciplinary collaboration can improve the early diagnosis and treatment of dental and systemic diseases, including CVDs.
The objective of this study was to examine the prevalence of periodontal pathogens, specifically P. gingivalis, P. intermedia, A. actinomycetemcomitans, T. forsythia, T. denticola, and F. nucleatum, in age- and obesity-specific groups. Additionally, the study aimed to investigate the correlation between the presence of these bacteria and risk factors associated with cardiovascular disease. These risk factors include obesity, smoking, lipid disorders, and proinflammatory cytokines. We also calculated cardiovascular risk (relative risk of cardiovascular disease and SCORE), and the Finnish Diabetes Risk Index (FINDRISC).
Materials and methodsEthical aspects
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Local Ethics Committee of the Federal Research Center of Nutrition, Biotechnology and Food Safety (protocol code N3/2020 dated on 02/10/2020). The study period was from March 2020 to November 2023. Informed consent was obtained from all subjects involved in the study. The collected samples were analyzed in a de-identified manner in order to ensure the confidentiality of the participants.
Subjects and study design
The study included 189 Caucasian subjects [44 men (23%), mean age 48 ± 21 years, mean BMI 30.1 ± 7.7 kg/m2] stratified into groups based on age and body mass index (BMI) (31, 32). Due to the lack of published data, the preliminary sample size calculation was uninformative. As a result, as much as possible eligible participants were enrolled. 105 individuals were young (18–45 years), of whom 57 were obese (BMI≥30 kg/m2; Young Obese) and 48 were not obese (BMI 18.5–29.9 kg/m2; Young Control). 84 participants were older (60–84 years), of whom 57 were obese (BMI≥30 kg/m2; Older Obese) while the remaining 27 were non-obese (BMI 18.5–29.9 kg/m2; Older Control). Subjects aged 45–60 years were not included in the study to make the differences between groups more prominent and to exclude overlap between groups. All participants underwent examination at the Nutrition Clinic of the Federal Research Centre for Nutrition, Biotechnology and Food Safety. The self-reported oral health data and dental care usage information were obtained through the completion of an electronic questionnaire, in which the participants were required to select the most appropriate answer option. The questionnaire included items on the presence of bruxism, bleeding on brushing, dentin hypersensitivity, use of dentures, and frequency of dental visits. Cardiovascular risk was calculated using the Systematic Coronary Risk Evaluation (SCORE) risk scales. The diabetes risk score of each individual was calculated by the Finnish Diabetes Risk Score (FINDRISC tool) (33). The flowchart illustrating the methodology for participant allocation, as well as the inclusion and exclusion criteria, is presented in Figure 1.
Flowchart visualizing participant recruitment. A total of 189 participants were enrolled in the study. Individuals were stratified into age and weight-adjusted groups.
Body composition measurements
Body weight and height were measured on a medical scale and stadiometer and performed as kg and m. BMI was calculated from weight and height using the BMI = weight (kg)/Height2 (m2) formula. Body fat mass (kg), muscle mass (kg), relative fat mass (%) etc. were measured by bioimpedance analysis on InBody 770 analyzer (Inbody Co. Ltd, Republic of Korea). The patient is required to fasten for at least 4 h in advance. In addition to the directly measured parameters, the fat-to-muscle ratio was calculated (34).
Glucose, lipid profile, and cytokines determinations
Venous blood was drawn by qualified medical personnel from each of the participants after overnight fasting from the antecubital vein using Vacutainer tubes (Unimed, Russia) for biochemical and enzyme-linked immunosorbent assay (ELISA) analysis of the serum. The results of the lipid profile (Triglycerides — TG, HDL-c, LDL-c, VLDL-c, and total cholesterol — TC), glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), uric acid were provided by standard laboratory procedures on «KONELAB Prime 60i» Laboratory analyser (Thermo Fisher Scientific, USA). In addition to the directly measured parameters, we calculated the following additional indices: non-HDL, LDL/HDL ratio, TG/HDL ratio, TG/LDL ratio, and the atherogenic index (35–37). Serum levels of Interleukin-1 beta (IL-1β), Interleukin 6 (IL-6), Tumor necrosis factor alpha (TNF-α) were detected using ELISA kits (Cloude-Clone Corp., China) following the manufacturer's instructions.
Oral samples collection
Oral samples were collected in the morning, at least 8 h after the last tooth brushing and food/liquid intake. Participants were asked to rinse their mouths with clean and sterile water and waited for approximately 5 min. Biofilm samples were collected from the outer surface of teeth and supragingival plaque for 30 s using sterile cotton swabs. Cotton swabs were placed in one tube containing 1.5 ml of phosphate-buffered saline (PBS) and mixed for 30 s. Saliva samples were collected in sterile polypropylene tubes using the spitting method (3–5 ml over 3 min) (38). Biofilm and saliva samples were pooled and stored at −80°C until nucleic acid extraction was performed.
PCR analysis
DNA extraction was performed by the phenol-chloroform method using the Lira + kit (Biolambix, Russia) according to the manufacturer's instructions. A Nanodrop 1,000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) was used to assess both the purity of DNA (via absorption ratios of the extracts at A260/A280) and the quantity of DNA. Then, using the specific 16S rRNA primers described in Table 1, the analysis of microbiota (P. gingivalis, P. intermedia, A. actinomycetemcomitans, T. forsythia, T. denticola, and F. nucleatum) was examined by PCR (39).
Oligonucleotides used for PCR analysis.
Bacterial species
Primers
Annealing temperature, °C
Product length, bp
P. gingivalis
for- AGGCAGCTTGCCATACTGCG
65
404
rev- ACTGTTAGCAACTACCGATGT
P. intermedia
for- CGTGGACCAAAGATTCATCGGTGGA
64
259
rev- CCGCTTTACTCCCCAACAAA
A.actinomycetemcomitans
for- GCTAATACCGCGTAGAGTCGG
68
443
rev-ATTTCACACCTCACTTAAAGGT
T. forsythia
for- GCGTATGTAACCTGCCCGCA
60
641
rev- TGCTTCAGTGTCAGTTATACCT
T. denticola
for- TAATACCGAATGTGCTCATTTACAT
60
316
rev- TCAAAGAAGCATTCCCTCTTCTTCTTA
F. nucleatum
for- AGAGTTTGATCCTGGCTCAG
60
360
rev- GTCATCGTGCACACAGAATTGCTG
For PCR, the prepared reaction mixture was used with the 5Х qPCRmix-HS (Evrogen, Russia); amplification was performed using a BioRad iQ cycler (Bio-Rad, Hercules, CA, USA). The reaction pattern was as follows: primary denaturation at 95°C for 10 min; denaturation at 95°C for 30 s; primer annealing at 60°C–68°C for 40 s; elongation at 72°C for 45 s (40 cycles). PCR products were separated by electrophoresis on 2% agarose gel (Figure 2) and analyzed with Gel Doc XP Workstation (Bio-Rad, USA). Due to the insufficient DNA concentration present in several of the analyzed samples, it was not possible to obtain PCR amplification results for all targeted bacteria (P. gingivalis n = 182, P. intermedia n = 182, A. actinomycetemcomitans n = 175, T. forsythia n = 176, T. denticola n = 176, F. nucleatum n = 176).
Examples of amplification of species specific amplicons of periodontal pathogenic bacteria by PCR.
Statistics analysis
The normal distribution of the data was assessed using the Kolmogorov-Smirnov test. A chi-square test was used to calculate the frequency distributions, and a non-parametric Mann–Whitney U-test was used to calculate the differences in continuous variables between conception outcomes. Associations between the presence of periodontal bacteria in oral samples and the main characteristics of the participants, biochemical parameters, and calculated SCORE and FINDRISC indices were performed using Tau-b-Kendall's correlation analysis. A p-value of <0.05 was considered to be statistically significant. IBM SPSS Statistics v22 (IBM Corp., Armonk, NY, USA) was used for all calculations. Set analysis and Venn diagram construction were performed using the InteractiVenn web tool (40).
ResultsClinical characteristics of the study groups
The analysis of demographics and chronic diseases prevalence was conducted. The findings of the comparative analysis by cohort are presented in Table 2. No significant gender differences were found. Furthermore, no notable age variance was observed between the two age groups. In contrast to the young control group, the young obese group had hypertension (63.1%), dyslipidemia (38.5%), and nonalcoholic fatty liver disease (NAFLD) (35.1%) as significantly prevalent. Additionally, a modest increase in the prevalence of smoking was observed among individuals in the young obese group (35.0% vs. 27.1%). For older individuals, the differences in the prevalence of chronic diseases were considerably less pronounced. The Older Obese group included more participants with NAFLD and more smokers.
The baseline characteristics of study groups (categorical parameters).
Parameter
Young individuals
Older individuals
p valuea
p valueb
Young obese (n = 57)
Young control (n = 48)
p value
Older obese (n = 57)
Older control (n = 27)
p value
Gender, N (%)
M—18 (31.6)F—39 (68.4)
M—13 (27.1)F—35 (72.9)
0.670
M—10 (17.5)F—47 (82.5)
M—3 (11.1)F—23 (88.9)
0.480
0.083
0.052
Age [years]
32 [28;39]
28 [26;34.7]
0.061
64 [62;67]
68 [62;74]
0.059
0.001
0.001
Hypertension, N (%)
36 (63.1)
0 (0)
0.001
52 (91.2)
22 (81.4)
0.410
0.001
0.001
Chronic heart failure, N (%)
1 (1.7)
0 (0)
0.112
31 (54.4)
11 (40.7)
0.950
0.001
0.001
Glucose Intolerance, N (%)
8 (14)
1 (2.1)
0.018
14 (24.5)
3 (11.1)
0.360
0.373
0.001
Type 2 diabetes, N (%)
4 (7.0)
0 (0)
0.74
13 (22.8)
4 (14.8)
0.600
0.006
0.001
Dyslipidemia, N (%)
22 (38.5)
0 (0%)
0.001
43 (75.4%)
15 (55.5)
0.600
0.001
0.001
NAFLD, N (%)
20 (35.1)
0 (0)
0.001
28 (49.2)
5 (18.5)
0.011
0.239
0.001
CAD, N (%)
5 (8.7)
0 (0)
0.05
19 (33.3)
8 (29.6)
0.450
0.004
0.001
Current smoking, N (%)
20 (35.0)
13 (27.1)
0.043
10 (17.5%)
2 (7.4%)
0.045
0.114
0.130
p values ≤0.05 are shown in bold.
Comparative analysis was conducted between the young obese and older obese groups.
Comparative analysis was conducted between the young control and older control groups.
The findings indicated a correlation between elevated blood pressure levels and the presence of obesity among both younger and older participants. The data highlighted that blood pressure values (sBP, dBP, mBP) were dependent on obesity and age. Blood pressure was higher in both obese and older participants. However, the age of the participants appeared to exert a greater influence on blood pressure than BMI. The increase in age was generally characterized by an increase in fat, including visceral fat, and a decrease in muscle mass and basal metabolic rate. The data is presented in Table 3.
The baseline characteristics of study groups (continuous parameters). Data are presented as median and interquartile range.
Parameter
Young individuals
Older individuals
p valuea
p valueb
Young obese (n = 57)
Young control (n = 48)
p value
Older obese (n = 57)
Older control (n = 27)
p value
sBP, mmHg
125 [120;140]
120 [112;120]
0.001
140 [130;150]
130 [130;140]
0.023
0.003
0.001
dBP, mmHg
80 [80;90]
80 [70;80]
0.001
90 [80;90]
80 [80;90]
0.008
0.017
0.001
mBP, mmHg
95.0 [93.3;106.7]
93.3 [87.5;93.3]
0.001
106.7 [96.7;111.6]
96.7 [93.3;106.7]
0.015
0.001
0.001
Height, cm
170 [165;178]
174 [167;180]
0.097
163 [157;168]
162 [159;167]
0.790
0.001
0.001
Body weight, kg
102 [86.7;131.1]
65 [57.2;71.0]
0.001
100.0 [92;121.5]
70 [65;74]
0.001
0.258
0.080
BMI, kg/m2
35.1 [31.2;42.1]
21.2 [20.0;22.9]
0.001
37.8 [34.5;43.6]
26.2 [24.6;28.4]
0.001
0.143
0.070
Fat mass, kg
42.2 [34.3;57.8
14.9 [11.9;19.0]
0.001
47.7 [41.1;57.4]
27.0 [23.6;30.2]
0.001
0.920
0.010
Visceral adipose tissue area, cm2
200.0 [166.3;238.7]
65.8 [51.1;84.2]
0.001
242.2 [211.5;266.5]
141.4 [125.6;155.9]
0.001
0.002
0.010
Muscle mass, kg
32.9 [27.9;40.7]
27.0 [23.0;31.6]
0.001
28.1 [25.6;32.1]
24.6 [22.2;25.7]
0.001
0.920
0.010
Fat-to-muscle ratio
1.3 [1.0;1.6]
0.6 [0.4;0.7]
0.001
1.7 [1.4;1.9]
1.2 [1.0;1.3]
0.001
0.001
0.001
Total body water, L
45.2 [37.3;51.1]
36.6 [31.0;43.5]
0.001
37.2 [34.4;41.9]
32.8 [30.2 ± 34.5]
0.001
0.001
0.005
Basal metabolic Rate, kcal
1,629 [1,458;1,899]
1,485 [1,291;1,674]
0.001
1,464 [1,387;1,586]
1,332 [1,258;1,385]
0.001
0.010
0.010
p values ≤0.05 are shown in bold.
Comparative analysis was conducted between the young obese and older obese groups.
Comparative analysis was conducted between the young control and older control groups.
Furthermore, significant differences were observed in biochemical parameters between the cohorts. The young obese group exhibited elevated alanine aminotransferase (ALT) values, although these remained within the normal range. Additionally, higher uric acid levels, along with altered lipid metabolism parameters [total cholesterol (TC), triglycerides (TG), and low-density lipoproteins (LDL)], and lower high-density lipoprotein (HDL) levels were observed. Among the elderly obese participants, a similar trend was noted, with elevated uric acid levels and reduced HDL concentrations in plasma. The appropriate cardiovascular risk assessment criteria were employed for the various age groups. It was found that obesity was a significant contributor to the observed increases in both cardiovascular risk (SCORE) and diabetes risk (FINDRISC) indexes. The Older Obese group was dominated by participants at moderate and high risk on the SCORE scale, while the Older Control group was dominated by participants at moderate risk. According to the FINDRISC scale, the young obese group had a higher proportion of moderate-risk individuals, while the young control group had a higher proportion of low-risk individuals; the obese older adults had a higher proportion of high-risk participants, while the non-obese group had a higher proportion of moderate-risk participants. The data is presented in Table 4.
Comparison of biochemical parameters and CVD and diabetes risk indexes between the study groups. Data are presented as median and interquartile range.
Parameter
Young individuals
Older individuals
p valuea
p valueb
Young obese (n = 57)
Young control (n = 48)
p value
Older obese (n = 57)
Older control (n = 27)
p value
AST, U/L
20.0 [17.7;30.4]
18.9 [16.5;20.8
0.100
21.5 [18.9;26.8]
26.5 [22.3;29.2]
0.100
0.312
0.050
ALT, U/L
22.1 [18.0;39.5]
15.0 [12.0;20.0]
0.001
19.0 [15.5;25.0]
20.0 [14.5;27.2]
0.940
0.050
0.104
Uric acid, µmol/L
353.1 [281.7;423.0]
263.8 [221.1;292.7]
0.001
376.5 [331.0;427.3]
271.9 [232.7;315.8]
0.001
0.218
0.536
Glucose, mmol/L
5.2 [4.6;5.4]
4.8 [4.6;5.0]
0.190
5.4 [4.8;6.0]
5.1 [4.8;5.5]
0.240
0.009
0.070
TC, mmol/L
5.0 [4.2;5.6]
4.5 [4.1;5.0]
0.044
5.3 [4.4;6.2]
5.7 [4.5;6.5]
0.460
0.115
0.010
TG, mmol/L
1.1 [0.8;1.5]
0.8 [0.6;1.0]
0.001
1.5 [1.1;2.1]
1.2 [1.0;1.7]
0.10
0.002
0.001
LDL cholesterol, mmol/L
3.3 [2.7;3.9]
2.6 [2.4;3.1]
0.001
3.3 [2.5;4.2]
3.4 [2.5;4.3]
0.930
0.866
0.008
HDL cholesterol, mmol/L
1.2 [0.9;1.3]
1.5 [1.2;1.7]
0.001
1.1 [1.0;1,4]
1.5 [1.3;1.7]
0.001
0.811
0.545
LDL/HDL ratio
0.4 [0.3;0.5]
0.6 [0.4;0.6]
0.001
0.4 [0.3;0.4]
0.4 [0.4;0.6]
0.003
0.782
0.056
Non-HDL mmol/L
3.8 [3.0;4.3]
3.1 [2.7;3.5]
0,001
4.1 [3.2;5.0]
4.1 [3.0;4.7]
0.469
0.063
0.003
TG/HDL ratio
1.0 [0.7;1.4]
0.6 [0.4;0.7]
0.001
1.3 [0.9;2.0]
0.9 [0.6;1.2]
0.002
0.007
0.004
TG/LDL ratio
0.4 [0.3;0.5]
0.3 [0.2;0.4]
0.013
0.5 [0.3;0.6]
0.4 [0.3;0.4]
0.022
0.001
0.015
Atherogenic coefficient
3.2 [2.6;4.1]
2.1 [1.8;2.5]
0.001
3.5 [2.7;4.6]
2.7 [2.0;3.3]
0.002
0.143
0.017
IL-1β, pg/ml
5.5 [4.5;8.5]
7.5 [4.7;10.2]
0.068
6.0 [5.0;8.0]
5.5 [5.0;7.5]
0.750
0.223
0.830
IL-6, pg/ml
1.6 [1.0;2.7]
2.0 [1.0; 3.2]
0.10
1.6 [1.0;2.0]
1.4 [1.0;2.2]
0.480
0.592
0.567
TNF-α, pg/ml
22.0 [14.0;29.0]
17.5 [12.0;27.2]
0.42
24.0 [15.5;27.5]
23.0 [13.0;25.5]
0.095
0.133
0.592
Relative CVD risk
2 [1;2]
1 [1;1]
0.001
SCORE
4 [3;6]
3 [2;4]
0.001
FINDRISC
11.0 [8;12.5]
3 [2;4]
0.001
16 [13;20]
11 [10;13.5]
0.001
0.001
0.001
p values ≤0.05 are shown in bold.
Comparative analysis was conducted between the young obese and older obese groups.
Comparative analysis was conducted between the young control and older control groups.
A number of oral health parameters were evaluated using self-reported data, including the prevalence of bruxism, the incidence of bleeding on brushing, the prevalence of dentin hypersensitivity, the use of dentures, and the frequency of dental visits. No significant differences were identified between the groups of young individuals with and without obesity. However, a trend towards a decrease in the frequency of dental visits was observed in the young obese group. No differences were identified in the group of elderly participants. Conversely, an increase in the prevalence of bleeding on brushing, use of dentures, and frequency of dental visits was observed when comparing young and elderly individuals (Table 5).
Comparison of several dental self-reported parameters between the study groups.
Parameter
Young individuals
Older individuals
p valuea
p valueb
Young obese (n = 57)
Young control (n = 48)
p value
Older obese (n = 57)
Older control (n = 27)
p value
Bruxism, N (%)
8 (14.0)
5 (10.4)
0.710
9 (15.7)
4 (14.8)
0.760
0.960
0.940
Bleeding on brushing, N (%)
18 (31.5)
13 (27.1)
0.580
31 (54.3)
12 (44.4)
0.560
0.030
0.070
Dentin hypersensitivity N (%)
18 (31.5)
18 (37.5)
0,540
20 (35.1)
9 (33.3)
0.920
0.750
0.790
Use of dentures N (%)
4 (7.1)
3 (6.2)
0,320
31 (54.3)
18 (66.7)
0.720
0.010
0.010
Dental visits, N (%)
Every 6 month
10 (17.5)
11 (22.9)
0.080
3 (5.3)
3 (11.1)
0.240
0.030
0.020
Every 1 year
14 (24.6)
19 (39.6)
11 (19.3)
11 (40.7)
Every 2–3 years
25 (43.9)
13 (27.1)
21 (36.8)
6 (22.2)
Every 3 + years
8 (14.0)
5 (10.4)
22 (38.5)
7 (26.0)
p values ≤0.05 are shown in bold.
Comparative analysis was conducted between the young obese and older obese groups.
Comparative analysis was conducted between the young control and older control groups.
The prevalence of the periodontal pathogens in the study groups
The prevalence of periodontal pathogens among participants was investigated using polymerase chain reaction (PCR) analysis. The overall prevalence of periodontal pathogens in the study population was 33.0% for P. gingivalis, 47.8% for P. intermedia, 63.4% for A. actinomycetemcomitans, 46.6% for T. forsythia, 46.6% for T. denticola, and 89.2% for F. nucleatum (Figure 3A). The gender differences were only confirmed for T. denticola (Figure 3B). This species was more prevalent in males (60.0% vs. 42.3%, p = 0.047). The prevalence of P. gingivalis did not differ significantly between the young obese and young control groups (16.4% vs. 15.2% p = 0.635). However, in older adults with obesity, the prevalence tended to be higher compared to non-obese individuals (60.0% vs. 40.0%, p = 0.084). The data revealed a significant effect of age on the prevalence of this species. Significant differences were observed in the prevalence of P. intermedia among younger participants, with a greater proportion in obese individuals (49.1% vs. 26.1%, p = 0.019). However, no differences were observed between the older age groups (58.9% vs. 60.0%, p = 0.928). A. actinomycetemcomitans was detected to be more prevalent among the older cohort of obese participants than in non-obese participants (78.6% vs. 46.2%. p = 0.004). T. forsythia was found to tend to be more common in young obese subjects, while no significant difference was identified. The occurrence of T. denticola exhibited a stronger correlation with age than BMI. The prevalence was 50.0% and 22.7% (p = 0.007) for the Young Obese and Young Control groups and 67.9% and 34.6% (p = 0.005) for the Older Obese and Older control groups, respectively. F. nucleatum was identified in nearly all the samples and was found to be independent of weight or age (Figure 3C).
The relative prevalence of major periodontal pathogens among all participants (A) compared by gender (B) and in the study groups (C) correlations between periodontal pathogens and age, gender, and BMI (D) correlation analysis for interbacterial associations (E) the Venn diagram is based on the analysis of the concordance of the prevalence of five microorganisms in the overall population (F) * p ≤ 0.05; ** p ≤ 0.01.
Furthermore, a set analysis was conducted to evaluate the prevalence of five specific microorganisms (P. gingivalis, P. intermedia, A. actinomycetemcomitans, T. forsythia, and T. denticola) within the entire population. The presence of five bacteria was identified in 13 (6.8%) participants. At the same time, a single bacterium was identified in the samples of 1, 8, 16, 12, and 5 participants, respectively (Figure 3F). All 6 bacteria were found in 12 (6.3%) participants, while none of the bacteria were identified in 16 (8.5%). The results of the correlation analysis indicated the presence of notable interactions between the species P. gingivalis and P. intermedia, as well as between T. forsythia and T. denticola. P. intermedia is associated with T. denticola, and A. actinomycetemcomitans is associated with T. denticola as well. Additionally, there is a notable correlation between T. forsythia and F. nucleatum (Figure 3E).
The relationship between periodontal pathogens and CVD risk factors
This study examined the link between periodontal pathogens and major risk factors for cardiovascular disease. A comparison of participants with and without periodontal pathogens revealed significant differences (Figure 4). The group of young obese individuals exhibited the highest number of parameters indicative of exposure to bacteria. Specifically, individuals with detected P. gingivalis demonstrated higher sBP (140 [137;146] mmHg vs. 120 [117;140], p = 0.001 mmHg, dBP 90 [85;93] mmHg vs. 82 [80;85] mmHg, p = 0.005 and MBP 107 [101;111] mmHg vs. 93 [90;102] mmHg, p = 0.002). The results demonstrated that P. intermedia was associated with lower HDL levels [0.98 [0.89;1.25] mmol/L vs. 1.34 [1.1;1.5] mmol/L, p = 0.001] and higher LDL/HDL ratios [3.2 [2.5;4.1] vs. 2.5 [1.9;3.3], p = 0.029] and atherogenic index [3.4 [2.9;4.5] vs. 2.9 [2.1;3.6], p = 0.047]. The presence of T. forsythia was associated with elevated triglyceride levels [1.2 [1.0;1.7] mmol/L vs. 1.0 [0.8;1.3] mmol/L, p = 0.028] and an increased atherogenic index [3.7 [2.7;4.3] vs. 2.9 [3.4;2.7], p = 0.036]. A positive link was observed between the presence of T. denticola and the Relative CVD risk (2.1 ± 0.7 vs. 1.7 ± 0.7, p = 0.031). Furthermore, in the young control group, P. intermedia was associated with higher LDL [2.9 [2.5;3.6] mmol/L vs. 2.5 [2.3;2.8] mmol/L, p = 0.039] and FINDRISC score [4 [3;6] vs. 3 [2;4], p = 0.043], although overall these values remained within the normal range. The prevalence of T. forsythia among older control participants was associated with higher body weight [72 [70;77] kg vs. 66 [59;72] kg, p = 0.048], but not with BMI (Figure 4).
Effects of the presence of periodontal bacteria on blood pressure, lipid metabolism, body weight, and CVD risk (relative CVD risk, SCORE) and FINDRISC scores in the study groups. Data are presented as median and interquartile range. * p ≤ 0.05; ** p ≤ 0.01.
The Tau-b-Kendall's correlation analysis was also conducted in order to identify some potential associations between periodontal pathogens and risk factors among all study participants (Figures 5A,B). Among all established diagnoses, only P. gingivalis was found to be significantly associated with hypertension and T. forsythia with NAFLD. A positive correlation was observed between T. denticola and smoking status. As observed in the young obese cohort, P. gingivalis was found to be correlated with all blood pressure parameters. Additionally, P. intermedia was associated with elevated sBP and mBP among all participants. The presence of P. intermedia and T. denticola was connected with higher SCORE in the younger cohort, whereas only T. denticola was linked to raised SCORE in the older group. Furthermore, a positive correlation was also indicated between FINDRISC and the presence of P. gingivalis, P. intermedia, A. actinomycetemcomitans and T. denticola. A positive correlation between periodontal bacteria and body composition parameters, particularly body weight, fat mass, and visceral fat area was also revealed. The strongest correlation with these parameters was observed for T. denticola. However, it is noteworthy that muscle mass and basal metabolic rate were also high. The presence of periodontal pathogens was also related to higher serum levels of TC, LDL, TG, glucose and lower levels of HDL and the LDL/HDL ratio in general. P. gingivalis was positively associated with TC, TG, and LDL. T. forsythia was associated only with LDL, while T. denticola was linked to TG. At the same time, T. forsythia and T. denticola were linked to elevated glucose levels. Additionally, higher uric acid levels were correlated with the presence of A. actinomycetemcomitans and T. denticola. Proinflammatory cytokine levels were not significantly associated with any periodontal pathogen in our study.
The Tay-b-kendall's correlation analysis was conducted to investigate the associations between periodontal pathogens and baseline participant characteristics (A) and biochemical parameters (B) among all study participants. * p ≤ 0.05; ** p ≤ 0.01.
Discussion
The oral microbiome is the second largest in terms of species and total number of microorganisms after the intestinal microbiome (41). A number of opportunistic bacteria are responsible for the development of such widespread diseases as periodontitis and caries, and can have a systemic effect on the body (21). The objective of this study was to assess the prevalence of six major periodontal pathogens according to age (young/old), gender, and obesity. In addition to the impact of microorganisms on CVD risk factors, including smoking, obesity, lipid and carbohydrate metabolism indicators, and SCORE and FINDRISC scales.
Overall, our findings indicate that both age and obesity are associated with a higher prevalence of periodontal pathogens. Specifically, P. gingivalis was more prevalent in obese and elderly individuals, while no significant difference was observed among young individuals. The prevalence of P. intermedia was higher in the young obese subjects compared to the control young group, while A. actinomycetemcomitans was more prevalent in the elderly obese subjects compared to the elderly non-obese. The prevalence of T. denticola was dependent on the BMI, as observed in both young and old individuals. A gender difference was found only for T. denticola, which was more frequent in males. The study demonstrated a comparable prevalence of P. gingivalis and P. intermedia.
According to the literature, the incidence of A. actinomycetemcomitans, F. nucleatum, and T. forsythia was markedly diminished in patients without type 2 diabetes. Furthermore, P. gingivalis was identified with greater frequency in overweight patients with type 2 diabetes mellitus (42). Another study revealed that adults over the age of 35 exhibited a higher prevalence of A. actinomycetemcomitans, whereas T. forsythia was more prevalent in younger adults. In addition, the prevalence of T. denticola differed by gender among the various bacterial species, with a higher prevalence observed in men (43). A study conducted in the Middle East and North African population without advanced periodontitis revealed a higher prevalence of P. gingivalis and P. intermedia, and a lower prevalence of A. actinomycetemcomitans. There was a statistically significant association between P. gingivalis and A. actinomycetemcomitans. There was no reliable correlation between P. intermedia and A. actinomycetemcomitans (44). In individuals from the Slovak population with periodontitis, a higher prevalence of T. denticola, P. gingivalis, T. forsythia, and A. actinomycetemcomitans was observed, with P. gingivalis being present in 100% of cases (45). It is noteworthy that another study identified a marginally lower overall prevalence of P. gingivalis and an inverse correlation with age, in addition to demonstrating the influence of ethnicity (46). The principal mechanism that may be accountable for the observed increase in the prevalence of oral diseases and the invasion of pathogenic microorganisms with age may be a reduction in the activity of innate immunity (47). Gender differences in the prevalence of oral microorganisms may be attributed to a number of factors, including a tendency for men to exhibit poorer oral hygiene and less frequent dental visits, as well as the potential influence of hormonal levels (48, 49). Furthermore, a notable correlation was identified between smoking status and the presence of T. denticola in the general population. The extant literature is inconclusive, with one study of 60 individuals indicating that smoking was associated with a higher prevalence of T. denticola and also with a suppressed inflammatory response (50). The findings of another study examining the effects of electronic cigarettes did not indicate a similar correlation (51).
The interaction of microorganisms with each other in the composition of coaggregates or biofilms plays a crucial role in providing their pathogenic effect (52). Moreover, opportunistic pathogens may be present in healthy individuals with intact periodontium (53). This study showed a correlation between the presence of periodontal pathogens. P. gingivalis was more frequently observed in the presence of P. intermedia and T. denticola. Similarly, P. intermedia and A. actinomycetemcomitans were more often detected in the oral cavity alongside T. denticola, while T. forsythia and F. nucleatum were commonly found together. A recent study showed that co-occurrence patterns may vary depending on the presence and severity of oral disease. For example, in individuals with healthy periodontium, P. gingivalis was more likely to co-occur with P. intermedia, whereas in periodontitis, P. gingivalis was associated with T. denticola and T. forsythia. T. forsythia was also found together with F. nucleatum (54). Furthermore, Fusobacteria, including F. nucleatum, are thought to bind early and late colonizers in dental plaque. The expression of galactose-specific lectin allows it to bind to P. gingivalis (55). Other studies have found that P. gingivalis stimulates the growth of T. denticola through the production of isobutyric acid, folate, and glycine. In turn, T. denticola produces succinic acid, which serves to enhance the growth of P. gingivalis (56, 57).
This study examined the relationship between the presence of periodontal pathogens and hypertension. The most significant association was found between hypertension and P. gingivalis, both at the level of diagnosis and at the level of sBP, dBP and mBP. Notably, when the study groups were considered, the difference was significant only in young adults with obesity. A positive correlation between high blood pressure and the presence of P. intermedia and T. denticola also found in the general population. It is established that the oral microbiome exerts an influence on blood pressure via its capacity to serve as an autonomous source of nitric oxide (NO), operating independently of the nitric oxide synthase (NOS) pathway (58). A variety of bacterial species are capable of producing nitric oxide in the oral cavity (59). The most extensively researched factor contributing to the maintenance of normal blood pressure is a relatively high level of Neisseria subflava and Corynebacterium durum in saliva. A notable decline in concentration was observed in individuals with hypertension (60, 61). A study of 653 participants demonstrated an association between elevated levels of the periodontal pathogens P. gingivalis, T. forsythia, A. actinomycetemcomitans, T. denticola and hypertension (62). Nevertheless, the precise mechanisms by which these bacteria may affect vascular tone remain unclear. A study conducted on C57BL/6J mice demonstrated that P. gingivalis may facilitate a reduction in angiotensin II levels (63).
The study did not reveal any notable correlation between the prevalence of periodontal pathogens and the incidence of obesity in the examined groups. Nevertheless, in the overall population, the most notable discrepancy was observed with the presence of T. denticola. Individuals who had this species exhibited differences in greater body weight, BMI, visceral fat area and a higher fat/muscle ratio. Furthermore, the findings indicated a correlation between P. intermedia and elevated BMI and fat mass across the entire study population. Data on the relationship between periodontal bacterial overgrowth and obesity are controversial. A recent study demonstrated a correlation between the presence of A. actinomycetemcomitans and obesity, with T. forsythia and T. denticola also identified in overweight individuals. In contrast, P. gingivalis and F. nucleatum were observed exclusively in those with a normal weight (64). Moreover, evidence suggests an association between an increased prevalence of F. nucleatum and P. intermedia in obese patients with periodontitis compared to those with a healthy metabolic profile (65). A study of 695 subjects demonstrated a correlation between the overgrowth of T. forsythia and the prevalence of overweight and obesity in individuals with a healthy periodontium (66). In another study, T. forsythia was demonstrated to be a contributing factor in the formation of a yellow coating on the tongue and to enhance the perception of taste for fatty foods (67).
A correlation was identified between lipid metabolism parameters and the presence of specific oral microorganisms, including P. gingivalis, P. intermedia, T. forsythia and T. denticola. It is noteworthy that the most significant differences were observed among the young obese group. Thus, HDL levels were found to be lower in individuals positive for P. intermedia, and the presence of T. forsythia was associated with higher LDL levels. In the overall population, serum concentrations of TC, LDL, and TG were found to positively correlate with the presence of P. gingivalis. Furthermore, a significant positive association was identified between T. denticola and TG levels (Figure 6). A meta-analysis comprising 29 studies demonstrated a connection between periodontitis and dyslipidemia. In particular, TC, LDL, and TG levels were significantly elevated in individuals with periodontitis, while HDL levels were reduced (68). Simultaneously, it was demonstrated that patients affected with periodontitis and dyslipidemia exhibited elevated incidences of bleeding on probing (BOP) and clinical attachment loss (CAL) (69). In an in vivo model of periodontitis induced by A. actinomycetemcomitans and P. gingivalis, it was demonstrated that a high-fat diet-induced dyslipidemia was associated with a notable elevation in systemic inflammation and bone loss (70). Another study in apolipoprotein E-deficient (ApoE−/−) mice showed that dyslipidemia impairs the innate immune response to P. gingivalis challenge, which may contribute to the increased activity of this species (71). Moreover, the combination of hyperlipidemia and periodontitis, but not only periodontitis, can lead to the development of atherosclerosis (72). A recent study has shown that periodontal metabolic parameters can serve as biomarkers for lipid and carbohydrate metabolism disorders in overweight and obese individuals (73). Furthermore, evidence indicates that P. gingivalis is associated with increased oxidative stress and lipid peroxidation, particularly in LDL (74). It is noteworthy that the administration of statins and fibrates for the treatment of dyslipidemia has been observed to diminish the likelihood of developing chronic periodontitis (75, 76). In a separate study, treatment with atorvastatin or simvastatin was observed to result in a reduction in the concentration of proinflammatory markers in the blood (IL-6, CRP, TNF-α), as well as a decrease in periodontal indices (77, 78). A recent study demonstrated that enhanced oral hygiene and concomitant reductions in the levels of P. gingivalis, T. denticola, and T. forsythia led to improvements in the hyperglycemic status of patients with T2DM, especially younger patients (79). Observed data are not able to clarify the cause-effect connections: either the dislipidemia causes the oral biota changes or periodontitis leads to dyslipidemia or even both blood lipids changes and oral pathogens growth are co-founders and caused by poor diet. Nevertheless, the link between them is well established. Furthermore, a potential covariation exists between the prevalence of periodontal pathogens and obesity, given that obesity is a well-established risk factor for CVDs (80, 81). Further research in this field may encompass additional investigations into the prevalence of periodontal pathogens across diverse age groups and ethnicities, as well as larger-scale studies. It is also crucial to examine interspecies bacterial interactions within the oral cavity, employing both relative and absolute quantification techniques. Furthermore, the potential mechanisms by which pathogenic microorganisms may exert adverse effects on overall health and well-being warrant investigation, including the utilization of biomarkers such as lipopolysaccharides (LPS), antibodies to periodontal pathogens, and an expanded panel of cytokines and adipokines. Further investigation is necessary to determine the causal relationships between oral microbiome dysbiosis and systemic diseases.
The relationships between periodontal pathogenic bacteria and factors such as age, obesity, hypertension, dyslipidemia and CVD risk are examined. Furthermore, the primary correlations between bacteria are presented.
Study limitations
This study did not examine in detail the presence of oral diseases such as caries or periodontitis, nor did it take into consideration of common periodontal indices such as periodontal pocket depth (PPD) and bleeding on probing (BOP), etc. This limitation is due to the therapeutic profile of the Nutrition Clinic of the Federal Research Center for Nutrition, Biotechnology and Food Safety. The objective of this study was to examine the presence of selected periodontal pathogens. However, the aim was not to quantify them. In addition, the insufficient number of men did not allow for gender-adjusted intergroup analysis.
Conclusion
The findings of this study underscore the significance of investigating the oral microbiome in the context of both oral health and systemic diseases, particularly concerning the correlation between periodontal pathogens and disorders such as obesity, dyslipidaemia, and hypertension. Another point of further research is the potential exists for obesity to serve as a connecting factor between oral dysbiosis and risk factors for CVDs. The high prevalence of these pathogens, including in young adults, underscores the potential benefits of preventive measures and early intervention. The plethora of studies revealing the systemic impact of periodontal disease highlights the necessity for prevention strategies to prioritise young adults, who are at a pivotal stage in establishing lifelong oral health habits. The collaboration between physicians and dentists is crucial in addressing these interconnected health issues. Medical professionals need to work together to ensure comprehensive care, recognizing that oral health is intrinsically linked to overall health. By integrating dental evaluations into routine medical check-ups, particularly for at-risk populations such as those with obesity or cardiovascular risk factors, healthcare providers can better manage and prevent the systemic effects of periodontal pathogens. This interdisciplinary approach is essential for mitigating the broader health implications of oral diseases and improving patient outcomes across the lifespan.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Local Ethics Committee of the Federal Research Center of Nutrition, Biotechnology and Food Safety. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Russian Science Foundation, grant number 22-15-00252, https://www.rscf.ru/en/project/22-15-00252/ (accessed on 16 August 2024).
Acknowledgments
The authors acknowledge the BioRender team for providing the artwork creation online service (BioRender.com).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
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