search for


Multiplex Real-Time Polymerase Chain Reaction Analysis of Pathogens in Peri-Implantitis and Periodontitis: A Randomized Trial
J Dent Hyg Sci 2024;24:97-106
Published online June 30, 2024;
© 2024 Korean Society of Dental Hygiene Science.

Eun-Deok Jo

Department of Dental Hygiene, College of Health Science, Gachon University, Incheon 21936, Korea
Correspondence to: Eun-Deok Jo,
Department of Dental Hygiene, College of Health Science, Gachon University, 191 Hambangmoe-ro, Yeonsu-gu, Incheon 21936, Korea
Tel: +82-32-820-4370, Fax: +82-32-820-4370, E-mail:
Received June 4, 2024; Revised June 11, 2024; Accepted June 17, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: Periodontitis and peri-implantitis are diseases caused by pathogenic microorganisms that cause tissue damage and alveolar bone destruction resulting in the loss of teeth and implants. Due to the biological differences in the tissues surrounding the implants, peri-implantitis progresses more rapidly and intensely than periodontitis, underscoring the importance of understanding the characteristics and interactions of pathogenic bacteria. This study aimed to quantitatively analyze the pathogenic microorganisms associated with periodontitis and peri-implantitis in Korean patients and evaluate the correlation between these bacteria.
Methods: A total of 98 (52 males and 46 females) were randomly selected and classified into three groups (healthy group [HG]=25; periodontitis group [PG]=31; and peri-implantitis group [PIG]=42). The relative expression levels of 11 pathogenic microorganisms collected from the gingival sulcus fluid were determined using multiplex real-time polymerase chain reaction.
Results: Eikenella corrodens, Fusobacterium nucleatum, and Prevotella nigrescens were highly prevalent in the HG, PG, and PIG patients. The results of the relative quantitative analysis of microorganisms showed that all bacteria belonging to the green, orange, and red complexes were significantly more abundant in the PG and PIG than in the HG (p<0.05). Porphyromonas gingivalis in the red complex showed a positive correlation with all microorganisms in the orange complex (p<0.05). Campylobacter rectus in the orange complex showed a significant positive correlation with all microorganisms in the red complex, and with F. nucleatum, P. nigrescens, Prevotella intermedia, and Eubacterium nodatum (p<0.05).
Conclusion: P. gingivalis, C. rectus, and F. nucleatum exhibit strong interactions. Removing these bacteria can block complex formation and enhance the prevention and treatment of periodontitis and peri-implantitis.
Keywords : Pathogenic microorganisms, Peri-implantitis, Periodontitis, Real-time polymerase chain reaction


Periodontitis and peri-implantitis are diseases in which teeth and implants are lost due to tissue damage and alveolar bone destruction caused by inflammation1). Pathogenic microorganisms are the leading causes2,3), forming bacterial films on both the root and implant surfaces4). Moreover, the crevices of all the surfaces of healthy or inflamed implants contain a greater variety of pathogenic microorganisms compared to the subgingival biofilms of natural teeth5). Peri-implantitis progresses faster and more intensely than general periodontitis because implants have different biological structures and less vascular distribution in the surrounding tissues1). Because peri-implantitis, such as periodontitis, begins with pathogenic microorganisms, attention should be paid to the characteristics and interrelationships of bacteria growing in colonies6,7).

The pathogenic microorganisms commonly found in periodontitis and peri-implantitis include Aggregatibacter actinomycetemcomitans, Prevotella intermedia, Streptococcus intermedius, Fusobacterium nucleatum, Eikenella corrodens, and Porphyromonas gingivalis8-11). Both diseases are associated with specific bacterial species or complexes12). Socransky et al.13) identified the relationship between species, location, and environmental variables based on staining response, colony type, clinical parameters, and pigments. Based on this, the change patterns were predicted and classified into five types of complexes. Pathogenic bacteria that significantly affect periodontal inflammation are classified into red and orange complexes13). The red complex, which is related to pocket depth and bleeding, is mainly found in the late stages of periodontitis, whereas the orange complex is found in the middle stages. Bacteria in these two complexes promote interactions between strains, which may affect the occurrence and progression of periodontal inflammation14). Bacteria formed in the early stages of periodontitis are classified as green complexes, while those found in healthy periodontal tissue are classified as yellow and purple complexes13). A. actinomycetemcomitans is the causative agent of rapid periodontal disease and is aggressive in localized areas. This is not classified as a cluster because the strain's “serum b” is different, but is mainly dealt with15).

It is essential to understand the characteristics, composition, and interactions of pathogenic microorganisms and develop strategies to prevent and treat diseases according to risk factors. Sculean et al.16) suggested various treatment methods for patients with periodontitis and peri-implantitis through bacterial analysis, and confirmed that the treatment effect was improved by applying them. Therefore, analysis of pathogenic microorganisms is essential. Existing culture-based methods have limitations such as difficulties in sample collection, low sensitivity and specificity, and cross-reactivity problems5). In addition, polymerase chain reaction (PCR), which applies molecular biology technology, can confirm the appearance rate of microorganisms; however, quantitative analysis is not possible17). However, by using multiple real-time PCR, the causative bacteria of periodontal and implant infections can be detected and identified rapidly. Therefore, it has been widely used in dentistry because it can identify and quantify periodontal pathogens in various clinical samples such as subgingival plaque, saliva, and gingival crevicular fluid7,18,19).


This study aimed to compare the appearance rate and quantity of 11 types of periodontitis-related pathogenic microorganisms in periodontitis and peri-implantitis in Korean patients using multiplex real-time PCR, and to investigate the correlation between the detected bacteria. Thus, it is possible to predict the treatment effect through bacterial reduction in periodontal and implant health management, and to establish an appropriate treatment plan.

Materials and Methods

1.Participant selection

This clinical study was approved by the Gachon Bioethics Committee. The study adhered to the ethical principles outlined in the Helsinki Declaration and followed the CONSORT guidelines20). The study was was registered in the Clinical Research Information Service (CRIS No. KCT0008890). G-power (version was used to determine the sample size for this study, with the effect size set to 0.5, significance level α to 0.05, and power to 0.95, and the calculated sample size was 80 people. Among the patients who visited the dental hospital, 130 who showed an interest in the study were selected. All participants agreed to participate in the study after receiving detailed information about the trial through oral and written study information sheets. In addition, 32 patients were excluded: patients with systemic diseases such as diabetes and hepatitis; patients with oral diseases excluding periodontitis; orthodontists; implant wearers used for overdentures; pregnant women; and antibiotic users. Finally, 98 patients (52 males, 46 females) participated in the study and signed a consent form.

2.Experimental procedure

Probing pocket depth (PPD) and bleeding on probing (BOP) tests were performed in all patients to evaluate the periodontal and peri-implant conditions. One dental hygienist examined four sites per tooth: #16, #11, #26, #31, #36, and #46 (mesial buccal, distal buccal, buccal, lingual, and palatal sides), using a WHO Probe (type C; DenMat, Lompoc., CA, USA). Patients were classified into healthy group (HG, n=25), periodontitis group (PG, n=31), and peri-implantitis group (PIG, n=42). HG indicates no BOP and PPD not exceeding 3 mm. PG indicates presence of BOP in the periodontal area around natural teeth PPD greater than or equal to 4 mm. PIG indicates presence of BOP around the implant that has been placed for more than 12 months as well as PPD exceeding 4 mm21).

Four samples were collected from the gingival crevicular fluid of each tooth and implant. A sterilized paper point (#35, 04 taper; DiaDent, Cheongju, Korea) was inserted into the deepest periodontal pocket and maintained for 30∼40 seconds. The paper points that absorbed gingival sulcus fluid was transferred to a 50 mL conical tube (sterilized; SPL Life Sciences, Pocheon, Korea), stored at 4°C or colder, and then submitted to the OO Bio Corporation for pathogen sampling and analysis.

3.Pathogen sampling and analysis

A total of 11 significantly pathogenic microorganisms13) were selected and quantitatively analyzed. As shown in Table 1, the pathogenic microorganisms were classified into clusters based on colony morphology, staining reactions, pigment production, and mutual correlation13), and the causative bacteria leading to acute periodontal disease15) were included. The genomic DNA (gDNA) of the pathogenic microorganisms was extracted using a column-type Exgene Cell SV DNA Isolation Kit (Exgene Cell SV; GeneAll Biotech, Seoul, Korea) according to the manufacturer’s instructionsDue to potential interference between forward and reverse primers designed for specific microbial DNA sequences, we grouped three to four strains into three separate panels (Table 1). These primers were used to analyze gDNA and clone standards isolated from the samples. Subsequently, the prepared forward and reverse primers and probes were mixed, and the analysis was conducted using a CFX96-IVD real-time PCR detection system (Bio-Rad Inc., Hercules, CA, USA). The input values before the analysis were the gDNA of standard strains sold by the Korean Collection for Type Cultures (KCTC, Korea), Korean Culture Center of Microorganisms (KCCM, Korea), and Korean Collection for Oral Microbiology (KCOM, Korea). The conditions were set once at 95°C for 5 minutes, and 41 times at 95°C for 30 seconds, 60°C for 40 seconds, and 72°C for 30 seconds. The strain was quantitatively analyzed based on the total gDNA amount of each sample. The analysis results were converted into an index to easily compare the differences by simply expressing the numerical value calculated as the average value obtained twice per sample.

Used Strains in This Study

Scientific name Target strain Popular
A pannel Aggregatibacter actinomycetemcomitans KCCM 12227 ATCC 29522
Porphyromonas gingivalis KCTC 5332 ATCC 33277
Tannernella forsythia KCTC 5666 ATCC 43037
Treponema denticola KCTC 15104 ATCC 35405
B pannel Fusobacterium nucleatum KCTC 2640 ATCC 25586
Prevotella nigrescens KCTC 15081 ATCC 33563
Prevotella intermedia KCTC 9692 ATCC 25611
Total bacteria 16s rDNA
C pannel Eubacterium nodatum KCTC 15015 ATCC 33099
Parvimonas micra KCOM 1535 ATCC 33270
Campylobacter rectus KCTC 5636 ATCC 33238
Eikenella corrodens KCTC 15198 ATCC 23834

KCCM: Korean Culture Center of Microorganisms, ATCC: American Type Culture Collection, KCTC: Korean Collection for Type Cultures, KCOM: Korean Collection for Oral Microbiology.

4.Statistical analysis

Data were statistically analyzed using SPSS for Windows (version 29.00; IBM Corp., Armonk, NY, USA). First, the characteristics of the patients and sampling sites were analyzed using one-way ANOVA and Bonferroni post-hoc tests. The prevalence and number of pathogens in each group were evaluated using Kruskal–Wallis and Bonferroni post-hoc tests. Correlations between the 11 pathogenic microorganisms were analyzed using Pearson's correlation coefficient. The statistical significance was set at p<0.05.


1.Characteristics of patients and sampling sites

Table 2 presents the mean age and sex of patients in each group. HG was 39.72 years (males: 28.0%; females: 72.0%), PG was 54.81 years (males: 61.3%; females: 38.7%); and PIG, 62.31 years (males: 61.9%; females: 38.1%). Table 2 summarizes the characteristics of the sampling sites. The PPD and BOP of PG (6.09 mm, 27.58%, p<0.001) and PIG (6.05 mm, 31.36%, p<0.001) showed a significant difference from the HG (3.19 mm, 2.96%), but there was no difference between PG (6.09 mm, 27.58%) and PIG (6.05 mm, 31.36%) (Table 2).

Characteristics of Patients and Sampling Sites

HG (n=25) PG (n=31) PIG (n=42) p-value
Age (y) 39.72±10.32 54.81±10.85 62.31±8.06
Males:females (%) 07 (28.0):18 (72.0) 19 (61.3):12 (38.7) 26 (61.9):16 (38.1)
PPD (mm) 3.19±0.41 6.09±1.44 6.05±1.16
p-value <0.001a <0.001b 0.893c
BOP (%) 2.96±3.87 27.58±10.87 31.36±11.84
p-value <0.001a <0.001b 0.351c

Values are presented as mean±standard deviation or ratio.

HG: healthy group, PG: periodontitis group, PIG: peri-implantitis group, PPD: probing pocket depth, BOP: bleeding of probing.

aSignificant difference between the HG & PG.

bSignificant difference between the HG & PIG.

cSignificant difference between the PG & PIG.

The p-values are the result of one-way ANOVA and the Bonferroni post-hoc analysis.

2.The relative expression rate of bacterial pathogens in three groups by real-time PCR

P. gingivalis, Tannernella forsythia, and Treponema denticola in the red complex appeared in the order of PIG (90.5%, 59.5%, and 61.9%, respectively), PG (87.1%, 35.5%, and 48.4%, respectively), and HG (20.0%, 16.0%, and 16.0%, respectively) (p=0.001). P. intermedia and Parvimonas micra in the orange complex appeared at similarly high levels in PIG (76.2% and 97.6%, respectively) and PG (71.0% and 100%, respectively) and at low levels in HG (8.0% and 76.0%, respectively) (p=0.001). Eubacterium nodatum and Campylobacter rectus appeared in the following order: PIG (69.0% and 85.7%, respectively), PG (64.5% and 77.4%, respectively), and HG (8.0% and 28.0%, respectively) (p<0.001) (Table 3).

The Relative Expression Rate of Bacterial Pathogens in Three Groups by Real-time PCR

HG (n=25) PG (n=31) PIG (n=42) p-value
Red Porphyromonas gingivalis 5 (20.0) 27 (87.1) 38 (90.5) <0.001
Tannernella forsythia 4 (16.0) 11 (35.5) 25 (59.5) 0.001
Treponema denticola 4 (16.0) 15 (48.4) 26 (61.9) 0.001
Orange Fusobacterium nucleatum 23 (92.0) 30 (96.8) 42 (100) 0.184
Prevotella nigrescens 18 (72.0) 28 (90.3) 37 (88.1) 0.120
Prevotella intermedia 2 (8.0) 22 (71.0) 32 (76.2) <0.001
Parvimonas micra 19 (76.0) 31 (100) 41 (97.6) 0.001
Eubacterium nodatum 2 (8.0) 20 (64.5) 29 (69.0) <0.001
Campylobacter rectus 7 (28.0) 24 (77.4) 36 (85.7) <0.001
Green Eikenella corrodens 25 (100) 30 (96.8) 41 (97.6) 0.683
Aggregatibacter actinomycetemcomitans 0 (0.0) 1 (3.2) 4 (9.5) 0.195

Values are presented as n (%).

HG: healthy group, PG: periodontitis group, PIG: peri-implantitis group.

The p-value is the result of the chi-square test.

3.Amount of bacterial pathogens in three groups by real-time PCR

All pathogens in the red and orange complexes showed differences in the abundance between HG and PG (p<0.05), and HG and PIG (p<0.01). The number of microorganisms in the PG and PIG was higher than that in the HG. However, there was no significant difference between PG and PIG. Similarly, E. corrodens in the green complex showed significant differences between HG and PG (p<0.05), and HG and PIG (p<0.05). A. actinomycetemcomitans did not differ between the groups (Table 4).

Amount of Bacterial Pathogens in Three Groups by Real-Time PCR

HG (n=25) PG (n=31) p
PIG (n=42) p
Red Porphyromonas gingivalis 0.42±2.08 18.24±28.95 <0.001 30.92±33.60 <0.001 0.389 <0.001
Tannernella forsythia 0.21±0.87 7.47±16.55 0.021 8.33±12.94 0.001 0.241 <0.001
Treponema denticola 0.00±0.01 0.37±0.62 0.018 2.29±8.32 <0.001 0.539 <0.001
Orange Fusobacterium nucleatum 50.31±68.35 163.08±120.71 <0.001 135.82±95.74 <0.001 >0.999 <0.001
Prevotella nigrescens 4.01±9.60 13.28±17.71 0.007 15.13±21.87 0.002 >0.999 0.001
Prevotella intermedia 0.59±2.13 11.64±22.99 <0.001 18.18±28.11 <0.001 0.922 <0.001
Parvimonas micra 0.27±0.76 3.45±4.00 <0.001 5.48±5.87 <0.001 0.745 <0.001
Eubacterium nodatum 0.13±0.45 4.45±8.67 <0.001 5.12±7.23 <0.001 >0.999 <0.001
Campylobacter rectus 0.21±0.68 3.22±4.34 <0.001 2.01±2.41 <0.001 >0.999 <0.001
Green Eikenella corrodens 3.56±3.89 9.97±11.51 0.030 5.45±8.26 0.032 >0.999 0.013
Aggregatibacter actinomycetemcomitans 0.00±0.00 0.00±0.01 0.256 0.02±0.11 0.061 0.639 0.189
Total 188.01±180.93 460.72±291.58 <0.001 446.06±200.62 <0.001 >0.999 <0.001

Values are presented as mean±standard deviation.

All bacterial counts are expressed in units of 106 cells/mL.

HG: healthy group, PG: periodontitis group, PIG: peri-implantitis group.

The p-values result from Kruskal–Wallis test and the Bonferroni post-hoc test by group.

4.Pearson’s correlation between bacteria in sample sites

Among the red complexes, P. gingivalis and T. forsythia showed significant positive correlations (r=0.344, p<0.001). P. gingivalis in the red complex correlated with F. nucleatum (r=0.278, p<0.01), Prevotella nigrescens (r=0.234, p<0.05), P. intermedia (r=0.290, p<0.01), P. micra (r=0.221, p<0.05), E. nodatum (r=0.448, p<0.001) and C. rectus (r=0.338, p<0.001) in the orange complex. In addition, T. forsythia correlated with F. nucleatum (r=0.261, p<0.01), P. nigrescens (r=0.216, p<0.05), E. nodatum (r=0.481, p<0.001), and C. rectus (r=0.290, p<0.01). Among the orange complexes, P. nigrescens (r=0.253, p<0.05), P. intermedia (r=0.409, p<0.001), P. micra (r=0.610, p<0.001), E. nodatum (r=0.435, p<0.001), and C. rectus (r=0.260, p<0.01), and P. nigrescens correlated with P. micra (r=0.259, p<0.01) and C. rectus (r=0.323, p=0.001). P. intermedia correlated with P. micra (r=0.395, p<0.001), E. nodatum (r=0.381, p<0.001), and C. rectus (r=0.376, p<0.001). P. micra showed a significant positive correlation with E. nodatum (r=0.353, p<0.001), and E. nodatum with C. rectus (r=0.235, p<0.05). No correlation was observed between the green complexes and A. actinomycetemcomitans (Table 5).

Pearson’s Correlation between Bacteria in Sample Sites

Red Orange Green
P. g T. f T. d F. n P. n P. i P. m E. n C. r E. c A. a
Red P. g 1
T. f 0.344 1
T. d 0.131 0.017 1
(0.197) (0.871)
Orange F. n 0.278 0.261 0.013 1
(0.005) (0.009) (0.898)
P. n 0.234 0.216 0.115 0.253 1
(0.021) (0.032) (0.261) (0.012)
P. i 0.290 0.088 0.039 0.409 0.104 1
(0.003) (0.391) (0.706) (<0.001) (0.308)
P. m 0.221 0.130 0.035 0.610 0.259 0.395 1
(0.028) (0.202) (0.735) (<0.001) (0.009) (<0.001)
E. n 0.448 0.481 0.068 0.435 0.088 0.381 0.353 1
(<0.001) (<0.001) (0.504) (<0.001) (0.389) (<0.001) (<0.001)
C. r 0.338 0.290 0.223 0.260 0.323 0.376 −0.001 0.235 1
(<0.001) (0.003) (0.027) (0.009) (0.001) (<0.001) (0.992) (0.019)
E. c 0.067 −0.047 0.012 0.088 0.186 −0.037 0.082 −0.012 0.034 1
(0.509) (0.645) (0.908) (0.391) (0.067) (0.719) (0.423) (0.904) (0.741)
A. a −0.008 0.029 −0.022 0.054 −0.047 −0.059 0.035 0.093 0.073 −0.089 1
(0.941) (0.779) (0.831) (0.595) (0.643) (0.563) (0.731) (0.363) (0.477) (0.384)

HG: healthy group, PG: periodontitis group, PIG: peri-implantitis group.

P.g: Porphyromonas gingivalis, T.f: Tannernella forsythia, T.d: Treponema denticola, F.n: Fusobacterium nucleatum, P.n: Prevotella nigrescens, P.i: Prevotella intermedia, P.m: Parvimonas micra, E.n: Eubacterium nodatum, C.r: Campylobacterrectus, E.c: Eikenella corrodens, A.a: Aggregatibacter actinomycetemcomitans.

The p-value is the result of Pearson's correlation.



Both periodontitis and peri-implantitis are caused by interactions between bacteria in the oral cavity and have bacteriological similarities. However, as suggested in a systematic review by Rajasekar and Varghese22), studies on the differences in pathogenic bacteria present in periodontitis and peri-implantitis compared to those in healthy periodontal conditions are insufficient. Therefore, in this study, patients with healthy periodontal conditions, periodontitis, and peri-implantitis were classified into three groups. The appearance rate and quantity of 11 microorganisms highly related to periodontitis were identified, and the correlation between each bacterium was analyzed.

2.Key results and comparison

As a result of the analysis of the 11 microorganisms, E. corrodens of the green complex and F. nucleatum and P. nigrescens of the orange complex showed high appearance rates in all groups of Korean patients, without any difference between groups. Previous studies have identified F. nucleatum23,24) and P. nigrescens25,26) as microorganisms commonly found in healthy oral cavities. In addition, A. actinomycetemcomitans27), mainly observed because of its strong effect on local periodontitis, is rarely detected in Korean patients. The same results were confirmed in this study.

Microorganisms belonging to orange and red complexes are characterized by virulence factors that promote or sustain inflammatory processes28,29). In particular, the highly pathogenic red complex is closely associated with the clinical features of periodontal diseases, especially PPD and BOP. Orange complexes are important because they may attach and bind to various oral bacteria and connect symbiotic colonies of periodontal pathogens13). In this study, all microorganisms from the red complex as well as P. intermedia, P. micra, E. nodatum, and C. rectus from the orange complex showed a significantly higher incidence of periodontitis and PIG than in the HG (p≤0.001). Therefore, the pathogenic microorganisms most likely to cause periodontitis and peri-implantitis belong to the red complex, specifically P. gingivalis, T. forsythia, and T. denticola; some microorganisms of the orange complex were also detected at high frequencies. However, regarding the differences in microbial composition between periodontitis and peri-implantitis, Koyanagi et al.7) found that peri-implantitis has more diverse microbial composition than periodontitis. Salvi et al.1), Canullo et al.30), and Wang et al.31) reported that the red complex microbial composition showed similar microbial profiles with no differences between periodontitis and peri-implantitis. In addition, Tamura et al.32) reported that the microbial composition in peri-implantitis differs from that in healthy periodontal conditions. In this study, there was no difference in the microbial composition between the PG and PIG, and the only difference was between the HG and the other groups. Because these results vary depending on the patients, methods, and microbial analysis techniques, follow-up studies reflecting the development of analytical techniques should be conducted.

Relative quantitative analysis of microorganisms showed that the numbers of all microorganisms belonging to the green complex (p<0.05), orange complex (p<0.001), and red complex (p<0.001) were significantly higher in the PG and PIG than in the HG. Ebadian et al.33) reported that P. gingivalis was quantitatively high in both periodontitis and peri-implantitis patients, whereas T. forsythia, P. intermedia, and C. rectus were quantitatively high in patients with periodontitis. In addition, Dabdoub et al.34) reported that the amount of T. denticola in the red complex was higher in periodontitis than in peri-implantitis. However, except for A. actinomycetemcomitans in this study, all periodontitis-causing bacteria belonging to the green, orange, and red complexes were abundant in the PG and PIG. Therefore, the microorganisms distributed in periodontitis and peri-implantitis are expected to affect the growth and proliferation of each other.

Gambin et al.14) reported that the microorganisms in red and orange complexes affected each other. In this study, the correlation between P. gingivalis and T. forsythia in the red complex was strong, and showed a very high correlation with E. nodatum in the orange complex. In particular, P. gingivalis was positively correlated with all microorganisms in the orange complex. Based on this, it can be predicted that P. gingivalis links the red and orange complexes together. P. gingivalis may affect each other’s growth while interacting with the microorganisms in the orange complex. Similarly, C. rectus in the orange complex significantly correlated with all microorganisms in the red complex, including P. gingivalis, T. forsythia, and T. denticola. It was highly correlated with all the microorganisms in the same complex, except for P. micra. Additionally, F. nucleatum in the orange complex was highly correlated with all microorganisms within the complex, confirming that it plays a key role in microbial growth in the orange complex. Thus, the red and orange complex microorganisms in the oral cavity may interact with each other to promote proliferation and induce a complex inflammatory response that may aggravate periodontitis or peri-implantitis. In this study, the most influential microorganisms between the complexes were P. gingivalis and C. rectus, whereas F. nucleatum had a strong influence on the orange complex. Therefore, the preferential removal of specific microorganisms that are highly related to other microorganisms, such as P. gingivalis, C. rectus, and F. nucleatum, may prevent colony formation or block the connections between colonies, thereby preventing periodontal disease or delaying its exacerbation.


The most important aspect in treating periodontal disease and peri-implantitis known so far is antibacterial treatment in the pathological periodontal pocket and cleaning of the contaminated implant surface35-37). The results of this study emphasize the importance of suppressing the number of red and orange complex microorganisms and controlling their proliferation for the treatment and management of periodontitis and peri-implantitis. Since an increase in pathogens may be detected by identifying the composition of oral bacteria in both natural teeth and implant patients, it is possible to identify patients at risk of periodontitis and peri-implantitis in advance and find ways to treat them early. In addition, such bacteriological analysis and understanding may help dentists and dental hygienists provide oral healthcare methods to prevent periodontitis or peri-implantitis that are suitable for each patient.


This study has limitations in terms of generalization due to the small number of samples, use of various implant products, and the limited number of microorganisms evaluated. To compensate for these limitations, more patients and follow-up studies are required to analyze the correlation between pathogenic bacteria and disease severity. In addition, because various factors may influence the correlation between microbial strains, it is necessary to further clarify the complexity of the interactions between strains through additional research and clinical trials.


This study showed that the appearance rate and quantity of 11 microorganisms that cause periodontitis were similar in PG and PIG, and that there were significant differences between the HG and the two other groups. P. gingivalis in the red complex and C. rectus in the orange complex exhibited the most inter-complex interactions, whereas F. nucleatum exhibited significant interactions within the orange complex. These findings suggest that certain microorganisms are associated with periodontitis or peri-implantitis, and may influence disease development and progression through interactions. Eliminating microorganisms that play a significant role in periodontitis and peri-implantitis may help prevent these conditions and contribute to the development of treatment strategies and individualized treatment plans.



Conflict of interest

No potential conflict of interest relevant to this article was reported.

Ethical approval

This study was approved by the Institutional Review Board committee of Gachon Bioethics Committee (IRB No. 1044396-202305-HR-082-01). The study was carried out in compliance with the Helsinki Declaration and following the CONSORT guidelines and is registered in the Clinical Research Information Service (CRIS No. KCT0008890). All the patients provided informed consent after completing a form on the survey.



Data availability

Raw data is provided at the request of the corresponding author for reasonable reason.

  1. Salvi GE, Aglietta M, Eick S, Sculean A, Lang NP, Ramseier CA: Reversibility of experimental peri-implant mucositis compared with experimental gingivitis in humans. Clin Oral Implants Res 23: 182-190, 2012.
    Pubmed CrossRef
  2. Hajishengallis G, Darveau RP, Curtis MA: The keystone-pathogen hypothesis. Nat Rev Microbiol 10: 717-725, 2012.
    Pubmed KoreaMed CrossRef
  3. Colombo AP, Bennet S, Cotton SL, et al.: Impact of periodontal therapy on the subgingival microbiota of severe periodontitis: comparison between good responders and individuals with refractory periodontitis using the human oral microbe identification microarray. J Periodontol 83: 1279-1287, 2012.
    Pubmed KoreaMed CrossRef
  4. Al-Ahmad A, Wiedmann-Al-Ahmad M, Faust J, et al.: Biofilm formation and composition on different implant materials in vivo. J Biomed Mater Res B Appl Biomater 95: 101-109, 2010.
    Pubmed CrossRef
  5. Kumar PS, Mason MR, Brooker MR, O'Brien K: Pyrosequencing reveals unique microbial signatures associated with healthy and failing dental implants. J Clin Periodontol 39: 425-433, 2012.
    Pubmed KoreaMed CrossRef
  6. Heitz-Mayfield LJA, Lang NP: Comparative biology of chronic and aggressive periodontitis vs. peri-implantitis. Periodontol 2000 53: 167-181, 2010.
    Pubmed CrossRef
  7. Koyanagi T, Sakamoto M, Takeuchi Y, Maruyama N, Ohkuma M, Izumi Y: Comprehensive microbiological findings in peri-implantitis and periodontitis. J Clin Periodontol 40: 218-226, 2013.
    Pubmed CrossRef
  8. Torrungruang K, Jitpakdeebordin S, Charatkulangkun O, Gleebbua Y: Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Treponema denticola / Prevotella intermedia co-infection are associated with severe periodontitis in a Thai population. PLoS One 10: e0136646, 2015.
    Pubmed KoreaMed CrossRef
  9. Iușan SAL, Lucaciu OP, Petrescu NB, et al.: The main bacterial communities identified in the sites affected by periimplantitis: a systematic review. Microorganisms 10: 1232, 2022.
    Pubmed KoreaMed CrossRef
  10. Hultin M, Gustafsson A, Hallström H, Johansson LA, Ekfeldt A, Klinge B: Microbiological findings and host response in patients with peri-implantitis. Clin Oral Implants Res 13: 349-358, 2002.
    Pubmed CrossRef
  11. Persson GR, Renvert S: Cluster of bacteria associated with peri-implantitis. Clin Implant Dent Relat Res 16: 783-793, 2014.
    Pubmed CrossRef
  12. Teles R, Teles F, Frias-Lopez J, Paster B, Haffajee A: Lessons learned and unlearned in periodontal microbiology. Periodontol 2000 62: 95-162, 2013.
    Pubmed KoreaMed CrossRef
  13. Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL Jr: Microbial complexes in subgingival plaque. J Clin Periodontol 25: 134-144, 1998.
    Pubmed CrossRef
  14. Gambin DJ, Vitali FC, De Carli JP, et al.: Prevalence of red and orange microbial complexes in endodontic-periodontal lesions: a systematic review and meta-analysis. Clin Oral Investig 25: 6533-6546, 2021.
    Pubmed CrossRef
  15. Socransky SS, Haffajee AD: The bacterial etiology of destructive periodontal disease: current concepts. J Periodontol 63: 322-331, 1992.
    Pubmed CrossRef
  16. Sculean A, Deppe H, Miron R, Schwarz F, Romanos G, Cosgarea R: Effectiveness of photodynamic therapy in the treatment of periodontal and peri-implant diseases. Oral Biofilms 29: 133-143, 2021.
    Pubmed CrossRef
  17. Gersdorf H, Meissner A, Pelz K, Krekeler G; Göbel UB: Identification of Bacteroides forsythus in subgingival plaque from patients with advanced periodontitis. J Clin Microbiol 31: 941-946, 1993.
    Pubmed KoreaMed CrossRef
  18. Luo L, Xie P, Gong P, Tang XH, Ding Y, Deng LX: Expression of HMGB1 and HMGN2 in gingival tissues, GCF and PICF of periodontitis patients and peri-implantitis. Arch Oral Biol 56: 1106-1111, 2011.
    Pubmed CrossRef
  19. Kim HS, Lim SA: Comparison between bacterial culture method and multiplex PCR for identification of Fusobacterium nucleatum and Actinobacillus actinomycetemcomitans from the dental plaques. J Dent Hyg Sci 9: 249-255, 2009.
  20. World Medical Association: World Medical Association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310: 2191-2194, 2013.
    Pubmed CrossRef
  21. Lang NP, Berglundh T; Working Group 4 of Seventh European Workshop on Periodontology: Periimplant diseases: where are we now?--Consensus of the seventh European workshop on periodontology. J Clin Periodontol 38: 178-181, 2011.
    Pubmed CrossRef
  22. Rajasekar A, Varghese SS: Microbiological profile in periodontitis and peri-implantitis: a systematic review. J Long Term Eff Med Implants 32: 83-94, 2022.
    Pubmed CrossRef
  23. Field CA, Gidley MD, Preshaw PM, Jakubovics N: Investigation and quantification of key periodontal pathogens in patients with type 2 diabetes. J Periodontal Res 47: 470-478, 2012.
    Pubmed CrossRef
  24. Griffen AL, Beall CJ, Campbell JH, et al.: Distinct and complex bacterial profiles in human periodontitis and health revealed by 16S pyrosequencing. ISME J 6: 1176-1185, 2012.
    Pubmed KoreaMed CrossRef
  25. Maeda N, Okamoto M, Kondo K, et al.: Incidence of Prevotella intermedia and Prevotella nigrescens in periodontal health and disease. Microbiol Immunol 42: 583-589, 1998.
    Pubmed CrossRef
  26. Teanpaisan R, Douglas CW, Walsh TF: Characterisation of black-pigmented anaerobes isolated from diseased and healthy periodontal sites. J Periodontal Res 30: 245-251, 1995.
    Pubmed CrossRef
  27. Kim MJ, Han GS: Quantitative detection of peri-implantitis bacteria using real-time PCR. J Korean Soc Dent Hyg 21: 555-565, 2021.
  28. Pinto G, Silva MD, Peddey M, Sillankorva S, Azeredo J: The role of bacteriophages in periodontal health and disease. Future Microbiol 11: 1359-1369, 2016.
    Pubmed CrossRef
  29. Harvey JD: Periodontal microbiology. Dent Clin North Am 61: 253-269, 2017.
    Pubmed CrossRef
  30. Canullo L, Peñarrocha-Oltra D, Covani U, Botticelli D, Serino G, Penarrocha M: Clinical and microbiological findings in patients with peri-implantitis: a cross-sectional study. Clin Oral Implants Res 27: 376-382, 2016.
    Pubmed CrossRef
  31. Wang HL, Garaicoa-Pazmino C, Collins A, Ong HS, Chudri R, Giannobile WV: Protein biomarkers and microbial profiles in peri-implantitis. Clin Oral Implants Res 27: 1129-1136, 2016.
    Pubmed CrossRef
  32. Tamura N, Ochi M, Miyakawa H, Nakazawa F: Analysis of bacterial flora associated with peri-implantitis using obligate anaerobic culture technique and 16S rDNA gene sequence. Int J Oral Maxillofac Implants 28: 1521-1529, 2013.
    Pubmed CrossRef
  33. Ebadian AR, Kadkhodazadeh M, Zarnegarnia P, Dahlén G: Bacterial analysis of peri-implantitis and chronic periodontitis in Iranian subjects. Acta Med Iran 50: 486-492, 2012.
  34. Dabdoub SM, Tsigarida AA, Kumar PS: Patient-specific analysis of periodontal and peri-implant microbiomes. J Dent Res 92(12 Suppl): 168S-175S, 2013.
    Pubmed KoreaMed CrossRef
  35. Paul Rosen DC, Cochran David, Froum Stuart, McAllister Bradley, Renvert Stefan, Wang Hom Lay: Peri-implant mucositis and peri-implantitis: a current understanding of their diagnoses and clinical implications. J Periodontol 84: 436-443, 2013.
    Pubmed CrossRef
  36. Park KH, Han GS: The effects of professional tooth cleaning and plaque control instruction on reduction of peri-implantitis. J Dent Hyg Sci 12: 163-170, 2012.
  37. Kim YS, Oh MJ: The effect of following oral health care on implant patients. J Dent Hyg Sci 9: 491-496, 2009.

June 2024, 24 (2)
Full Text(PDF) Free

Social Network Service

Cited By Articles
  • CrossRef (0)
  • Download (37)

Author ORCID Information