Oral microbiota in periodontitis patients with and without type 2 diabetes mellitus and their shifts after the nonsurgical periodontal therapy

Abstract

Objective

To investigate the shift in the oral microbiota of periodontitis patients with and without type 2 diabetes mellitus (T2DM) undergoing nonsurgical periodontal treatment and its implications.

Methods

Eleven patients with chronic periodontitis and eleven patients with chronic periodontitis and diabetes from the Second Affiliated Hospital of Chongqing Medical University received nonsurgical periodontitis treatment and were re-evaluated 3 months later. DNA from the saliva and subgingival plaques was amplified and sequenced using 16S ribosomal RNA (16S rRNA) gene sequencing for microbiome profiling. Clinical indexes at the onset and after periodontal therapy were recorded and compared.

Results

The species richness and dominant microbiota of periodontitis patients with and without T2DM changed significantly after nonsurgical periodontal treatment in both saliva and subgingival plaques. The periodontal condition of the patients was also effectively improved three months after therapy. Glycemic control in patients with periodontitis and T2DM was improved. Additionally, nonsurgical periodontal therapy could increase in subgingival microbial diversity and the proportion of health-associated bacteria but a proportional reduction in pathogenic bacteria in periodontitis patients with T2DM. Network analysis revealed fewer links and a lower level of centralization in the chronic periodontitis (CP) group after treatment. However, more links and a higher network density of the networks were found in the CP + T2DM group, suggesting a more stable microbial community after treatment.

Conclusions

There were significant differences in both the structural composition and reaction of the oral microbiota to periodontal treatment between periodontitis patients with and without T2DM. Nonsurgical periodontal treatment can improve metabolic control, decrease the proportion of periodontal pathogens in oral conditions, and help stabilize microbial communities in patients with periodontitis and T2DM. Furthermore, nonsurgical periodontal treatment may be a potential supplementary approach for managing T2DM.

Graphical abstract

Highlights：

• •The oral microbiota structure of periodontitis patients with diabetes is different from that of non-diabetic patients.
• •Nonsurgical periodontal treatment can improve glycaemic control in patients with chronic periodontitis and diabetes.
• •Nonsurgical periodontal treatment can reduce the proportion of periodontal pathogens of periodontitis patients with diabetes.

1. Introduction

Periodontitis, a chronic inflammatory disease, is associates with complex dental bacterial plaque and is characterized by the loss of alveolar bone and periodontal tissue attachment and the formation of a periodontal pocket [1]. Type 2 diabetes mellitus (T2DM) is an important metabolic syndrome resulting from insulin resistance and hyperinsulinemia, which leading to glucose intolerance and hyperglycemia [2]. Several studies have established a reciprocal relationship between diabetes and periodontitis [3]. However, few other diseases (a mainly immune-mediated or neoplastic) so far were investigated in terms of salivary or plaque microbiome [4,5] and this is an emerging field of study. In patients with T2DM, there is a positive correlation between periodontitis and glycemia [6]. In general, the mechanisms by which glycemia predisposes T2DM patients to periodontitis might be partially attributed to its ability to induce high levels of pro-inflammatory cytokines and dysregulate the host immune response, which ultimately lead to dysbiosis in the oral microbiome [7,8]. Periodontal disease adversely affects glycemic control and complications in T2DM patients [9]. Periodontal therapy has been shown to reduce hemoglobin A1c or glycated hemoglobin (HbA1c) values in T2DM patients [10].

Although little studied, it has been reported that specific periodontal microbes are linked to T2DM [11]. Studies have showed that the prevalence of Tannerella forsythia (T. forsythia), Porphyromonas gingivalis (P. gingivalis), Capnocytophaga, Saccharibacteria(TM7), Aggregatibacter, Neisseria, and Gemella in the subgingival microbiota of T2DM patients was higher than that in patients without T2DM. The prevalence of Filifactor, Eubacterium, Synergistetes, and Treponema genera was lower in the subgingival biofilm of T2DM patients [11,12]. In addition to the change of subgingival microbiota, diabetes mellitus was also confirmed to prominently alter α- and β-diversity of the salivary microbiota (α-diversity is the biodiversity within a particular area, community or ecosystem; β-diversity is the comparison of species composition between different communities), while periodontal treatment did not result to the recovery of flora [13,14]. Moreover, the presence of P. gingivalis in periodontal pockets, especially clones with type II fimbriae, could increase the glycemic index in patients with diabetes. Poor glycemic control is associated with increased numbers of the red complex bacteria (P. gingivalis, T. forsythia, and Treponema denticola) in the periodontal pockets [15].

Nonsurgical periodontal treatment can improve glycemic control and reduce systemic inflammation in T2DM patients with periodontitis [16]. Studies has shown that HbA1c levels can be markedly reduced by 0.40 % after periodontal treatment for more than 3 months [16]. Despite the epidemiological evidence for that periodontal therapy could improve metabolic control and have selective implications on the species level of periodontal microbes in T2DM patients, more evidence is needed to confirm the interaction between periodontal treatment and metabolic control in patients with diabetes on a biological level [17].

Over the last decade, the high-throughput sequence analysis of the 16S ribosomal RNA (16S rRNA) gene has been widely used to determine oral microbiome composition [18]. However, few case-control studies have used 16S rRNA analysis to investigate changes in the microbiome composition of patients with periodontitis and T2DM after periodontal therapy. Therefore, the present study used sequencing to investigate the differences in the oral microbiomes of non-diabetic and diabetic cohorts with periodontitis before and after the nonsurgical periodontal treatment, which could reveal unique microbial characteristics in the oral cavity of patients with T2DM.

2. Materials and methods

This was a longitudinal analysis. It was conducted at a single location from January 2021 to December 2021, with a follow-up after three months. This study was performed in accordance with the guidelines of the World Medical Association Declaration of Helsinki and was approved by the Second Affiliated Hospital of Chongqing Medical University (Chongqing City, China) (protocol number: Ratification No. 459/2020). A total of 22 adult patients, all of whom were individually informed and signed informed consent forms, were included in this study.

2.1. Samples collection

non-T2DM patient inclusion criteria: diagnosed with chronic periodontitis but were in good general health.

T2DM patient inclusion criteria: diagnosed with chronic periodontitis and T2DM for at least the past 1 years.

The exclusion criteria were as follows: less than 12 remaining natural teeth per patient; usage of anticonvulsants, anti-inflammatories, antibiotics, immunosuppressants, or calcium channel blockers in the last 12 weeks; having received a course of periodontal treatment within the last 6 months; pregnancy or lactation; carriers of blood disorders; and suffering from any other systemic diseases, such as hypertension, hyperlipidemia, the metabolic comorbidity of T2DM, hepatic disease, nephropathy and cancer.

2.2. Basic information collection and clinical examination

At baseline, all patients received information collections regarding age, sex, height, weight, body mass index (BMI) and drug history. Full-mouth periodontal indices were measured at baseline and 3 months. Full-mouth periodontal examination included the following parameters: (i) Plaque Index (PI), (ii) Probed Pocket Depth (PD), and (iii) Clinical Attachment Level (CAL) at six sites per tooth.

2.3. Sample collection and laboratory examination

All participants were prohibited from eating and performing oral hygiene procedures for 12 h before sampling. The three types of samples obtained from each patient at the baseline and at the 3 months-visit were subgingival plaque (n = 22), unstimulated saliva (n = 22), and whole blood samples (n = 22). The sample collection process complies with standardized protocols. First, patients need to use tap water to rinse their mouths. The participants were then required to tilt their heads forward to facilitate unstimulated saliva spilling into sterile universal polypropylene container tubes. A subgingival plaque was collected using a dental curette and suspended in a 2 mL centrifuge tube. The above samples were stored in refrigerated storage (−80 °C) until further processing. As for blood investigations, whole blood samples (at least 2 mL) were drawn and analyzed in the Department of Laboratory of the Second Affiliated Hospital of Chongqing Medical University for fasting plasma glucose and glycated hemoglobin (HbA1c, %) analysis.

2.4. Periodontal intervention

At baseline, the patients were instructed to use the modified Bass technique to guarantee dental hygiene. Thereafter, patients were treated with nonsurgical periodontal therapy consisting of scaling and root planning (SRP) using an ultrasonic scaler and a manual periodontal curette to remove subgingival plaque and calculus. During the study period, the participants were forbidden from using antibiotics, anti-inflammatory drugs, or chlorhexidine mouthwashes.

At the 3 months-visit, the participants were given oral hygiene instructions for the second time. The same examiner performed a full-mouth periodontal assessment and SRP.

2.5. DNA extraction, library construction and sequencing

After bacterial DNA extraction, 1 % agarose gel electrophoresis was performed to determine the concentration and purity of the extracted genomic DNA. 338F/806R primers (ACTCCTACGGGAGGCAGCAG, GGACTACHVGGGTWTCTAAT, respectively) and a PCR kit (TransGenAP221-02) were used to amplify the V2–V4 region of the 16S rRNA. The extracted DNA was quantified by electrophoresis on a 2 % agarose gel and QuantiFluor™ -ST Blue Fluorescence Quantification System (Promega); samples with a single amplification product were chosen for further experiments. After constructing a qualified MiSeq library, the resulting library was then sequenced on an Illumina MiSeq sequencing platform at the Majorbio Cloud Platform (Shanghai, China).

2.6. Bioinformatics analyses

During data preprocessing, this study used the second-generation Quantitative Insights into Microbial Ecology (QIIME2 process version 1.0) software suite to perform quality control, filtration, and some modifications of the raw sequence. The sequence denoising method (DADA2 and Deblur) was used to process the data and identify the representative sequence and abundance information of Amplicon Sequence Variant (ASV). Based on ASV's representative sequence and abundance information, a series of statistical and visual analyses, such as community diversity and species difference analyses, were performed.

Alpha diversity was calculated using Simpson Evenness, Shannon diversity and observed richness. The differences between groups in alpha diversity index values were compared using the Kruskal-Wallis rank-sum test. Principal co-ordinates analysis (PCoA), similar to Principal Component Analysis (PCA), is a nonrestrictive data dimensionality reduction analysis method for computing the differences between microbial communities in terms of beta diversity. In addition, this study also used a heat map to analyze and represent the top ten bacterial specie abundances at two different oral sites at the genus levels. The intergroup species abundance differences at the genus levels were analyzed using multi-group comparison study.

2.7. Statistical analysis

Data pertaining to clinical parameters were analyzed using statistical SPSS26.0(IBM SPSS, Chicago, IL, USA) software, and normally distributed continuous variables were presented as x ± s (standard deviation). Between-group differences were assessed using an Independent-Samples T Test. The same patient's data at baseline and 3 months after therapy were compared using a Paired Student's t-test. The Kolmogorov-Smirnov test was used to evaluate alpha diversity. Beta diversity (between-sample community dissimilarity) was calculated using UniFrac distance, and its significance was determined using analysis of similarities (ANOSIM). MetagenomeSeq was used to create a heat map with the MRheatmap function. Microbial species difference analysis was computed via the Inter-group difference test. Taxonomical features with P value < 0.01 was considered significant microbial signatures. Microbial co-occurrence network construction and subgingival and salivary bacteria analysis were conducted using R packages vegan, igraph, and Hmisc. A significance level of less than 0.05 and a Pearson's coefficient of greater than or equal to 0.6 signified a significant correlation. Subsequently, a network diagram was plotted using the Gephi software (version 0.9.2) [19].

3. Results

After sifting through FBG, HbA1c, and periodontitis status, only a total of 22 patients were included in this study. No obvious adverse effects were observed during the 3-month experimental period. The 22 patients were categorized into the CP group (n = 11) or CP + T2DM group (n = 11), among which two patients took hypoglycemic drugs regularly, two patients used insulin to help maintain blood glucose levels, and seven patients had a family history of diabetes.

3.1. Demographic and clinical parameters of the patients

The general and clinical characteristics of the study participants are shown in Table 1. There were no significant differences among the groups in terms of age, sex, weight, alcohol consumption, body mass index, average depth of whole-mouth probing, plaque index, and adhesion loss. However, there were significantly more smokers, and higher fasting blood glucose and glycohemoglobin levels in the CP + T2DM group than in the CP group (P = 0.000).

Table 1.

General and clinical characteristics of two groups of patients [mean ± standard deviation (range)].

Characteristic	CP	CP + T2DM	P-value
	(n = 11)	(n = 11)
Age (years)	54.1 ± 13.1	55.7 ± 10.3	0.749
Gender (male/female)	5/6	5/6	/
Mean body mass index	23.6 ± 2.8	24.4 ± 3.7	0.584
Smoking (%)	9.1	9.1	/
Drinking (%)	18.2	45.5	/
Diabetes duration (years)	0	5.4 ± 5.8	/
Mean probing depth (mm)	3.3 ± 0.9	3.2 ± 0.8	0.640
Clinical attachment level (mm)	2.9 ± 1.4	3.5 ± 0.6	0.260
Plaque index	2.5 ± 0.5	2.4 ± 0.7	0.488
Fasting plasma glucose (dg/lL)	5.2 ± 0.5	7.6 ± 1.5	0.000
HbA1c (%)	5.5 ± 0.3	7.3 ± 1.3	0.001

3.2. Changes of periodontal clinical parameters and glucose metabolism parameters before and after periodontal treatment

Three months after periodontal treatment, significant improvements in the periodontal parameters were observed in both groups (Table 2). The mean probing depth and attachment loss of the whole mouth were significantly improved in both the CP and CP + T2DM groups (p < 0.05), and the PI in the CP + T2DM group was significantly decreased (p = 0.008), whereas there were no significant differences with respect to PI in the CP group. In addition, there was a significant improvement in fasting blood glucose and glycosylated hemoglobin levels in the CP + T2DM group at 3 months after periodontal nonsurgical treatment compared with baseline (P = 0.038; P = 0.028). There was no significant difference in the HBA1c value in the CP group; however, fasting blood glucose increased slightly (P = 0.008), which may be related to changes in the diet structure reported by individual patients during the experimental period.

Table 2.

Periodontal condition of patients before and after primary periodontal therapy.

	CP (n = 11)			CP + T2DM (n = 11)
	baseline	3 months	P-value	baseline	3 months	P-value
Mean probing depth (mm)	3.3 ± 0.9	2.5 ± 0.7	0.002	3.2 ± 0.8	2.8 ± 0.7	0.007
Clinical attachment level (mm)	2.9 ± 1.4	2.4 ± 1.0	0.048	3.5 ± 0.6	3.1 ± 0.6	0.009
Plaque index	2.5 ± 0.5	2.5 ± 0.5	0.000	2.4 ± 0.7	1.4 ± 0.5	0.008
Fasting plasma glucose (dg/lL)	5.2 ± 0.5	5.6 ± 0.8	0.008	7.6 ± 1.5	6.9 ± 1.1	0.038
HbA1c (%)	5.5 ± 0.3	5.6 ± 0.4	0.402	7.3 ± 1.3	6.7 ± 0.8	0.028

3.3. Analysis of bacterial diversity and community structure

The alpha and beta diversity analysis results showed that the abundance and predominant bacteria in the saliva and subgingival plaques were different. The species richness of bacteria in the subgingival plaque was significantly higher than that in saliva (p＜0.05). In addition, the abundance of periodontal pathogens in the subgingival plaque was higher than that in saliva (Fig. 1(a and b)). However, there was no significant difference in the alpha diversity of the oral microbiota between CP subjects and CP + T2DM subjects (P > 0.05, Fig. 1(a)).

Fig. 1.

(a) Shows α diversity (measured by Observed Richness, Shannon and Simpson Diversity Index) of saliva and subgingival plaques in periodontitis patients with and without T2DM; (b) shows β diversity of the above groups. (* 0.01 < P ≤ 0.05，** 0.001 < P ≤ 0.01，***P ≤ 0.001).

In addition, Species differences analysis showed that the prevalent genera among saliva of CP subjects were Streptococcus (20 %), Neisseria (11 %), P. gingivalis (9 %), Prevotella (8 %), Veryonella (7 %), and actinomyces (7 %); while the dominant genera among subgingival plaque of CP subjects were P. gingivalis (10 %), Actinomyces (9 %), Leptotrichia (7 %), Prevotella (6 %), and Corynebacterium (4 %). The dominant bacteria in the saliva of CP + T2DM patients were streptococcus (24 %), P. gingivalis (12 %), Rothia (7 %), Prevotella (7 %), and Neisseria (7 %); while the core subgingival microbiota among CP + T2DM subjects were Porphyromonas (20 %), Actinomyces (7 %), Fretibacterium (6 %), Leptotrichia (6 %), and Fusobacterium (5 %) (Fig. 2(a and b) and Fig. 3). Compared with CP subjects, CP + T2DM subjects had a higher proportion of total clones of Porphyromonas, Fretibacterium, Fusobacterium, Rothia, and Filifactor, as well as Eubacterium saphenum in subgingival plaque, but a lower proportion of total clones of Comamonadaceae and Comamonas in subgingival plaque and a lower proportion of Actinomyces, Alloprevotella, Oribacterium, Campylobacter, Lautropia in saliva (p < 0.05) (Fig. 4(a and b)).

Fig. 2.

(a) Shows the top 10 species and corresponding P values of saliva and subgingival plaque in chronic periodontitis patients (CP group) at the genus level, and (b) shows the top 10 species and corresponding P values of saliva and subgingival plaque in patients with chronic periodontitis and type II diabetes (CP + T2DM group) at the genus level.

Fig. 3.

Is a heatmap figure that shows the top 10 species of bacteria at the genus level in two different oral sites. The abscissa at the bottom represents the sample, and the left vertical coordinate represents the name for bacteria. Pink denotes a high relative abundance; red denotes a low relative abundance.

Fig. 4.

Shows the bacterial species with statistically significant differences in the saliva (a) and subgingival plaque (b) of subjects in the two groups at the genus level.

The abscissa is the portion of species abundance at the genus level, the ordinate is the name for each genus of bacteria. (* 0.01 < P ≤ 0.05，** 0.001 < P ≤ 0.01，***P ≤ 0.001).

3.4. Shift in oral bacteria after treatment at the genus level

The results of alpha and beta diversity analysis (Fig. 5(a and b)) showed that there was no significant difference in saliva and subgingival plaque of CP subjects before and after treatment (P＞0.05). In CP + T2DM subjects, the microbial richness and diversity in saliva and subgingival plaque increased after treatment (P＜0.05). The changes in microbiome composition at different oral sites are shown in Fig. 6(a–d). The levels of classical periodontal pathogens (Porphyromonas, Filifactor and Eubacterium) decreased, whereas the levels of health-associated or periodontitis-irrelevant bacteria (Selenomonas, Actinobacteria, Streptococcus, Veillonella, Actinomyces, Leptotrichia and Prevotella) increased [20].

Fig. 5.

(a) α diversity of saliva and subgingival plaque pre- and post-treatment, consisted of the Observed Richness, Shannon and Simpson indexes. There has statistically difference between samples of two oral sites and two time points. (b) PCA plots and PCoA plots. Each symbol represents one sample; green points represent pre-treatment communities; red points represent post-treatment communities.

Fig. 6.

Contrasting changes in the microbiome of saliva (a) and subgingival plaque (b) in chronic periodontitis subjects pre- and post-nonsurgical periodontal treatment at the level of genus; the shift in the microbiome of saliva (c) and subgingival plaque (d) in patients with periodontitis and T2DM at the genus level. (* 0.01 < P ≤ 0.05，** 0.001 < P ≤ 0.01，***P ≤ 0.001).

4. Correlation networks

This study revealed strongly connected microbial components at the genus level, and compared the differences in the networks of the subgingival microbiota pre- and post-treatment (Fig. 7(a–d)).

Fig. 7.

Correlation networks of the microbiome of subgingival plaque in CP group pre- (a) and post- (b) nonsurgical periodontal treatment; Correlation networks of the microbiome of subgingival plaque in CP + T2DM group pre- (c) and post- (d) treatment. Correlation network analysis was performed at the level of genus. Edges between each point indicate significant correlations. Red edges indicate positive correlations, but green edges indicate negative correlations. The size of each node is determined by the mean relative abundance. The thickness of the line is determined by the correlation coefficient.

Before treatment, the network of the subgingival microbiota in the CP group included 50 nodes and 570 edges. Additionally, the highest number of connecting edges were found in Clostridium_sensu_stricto_1, Sarcina, errisporobacter, Romboutsia, Turicibacter, Sedimentibacter, and Tissierella, and the above bacteria had a positive correlation with each other, while mainly had negative correlation with other genera; after treatment, the number of nodes decreased to 48, and the number of edges decreased to 167. In addition, Oribacterium and Dialisterhad had the largest number of connecting lines; the positive correlation of Oribacterium accounted for 53 %, and there was a predominance of positive correlations between g_Dialister and the other generac.

The pre-treatment network of the subgingival microbiota in the CP + T2DM group consisted of 47 nodes and 92 edges. In addition, Pyramidobacter and Desulfobulbus had the largest number of connecting lines and a predominance of positive correlations, whereas more links and a higher level of centralization were found in the post-treatment-associated network, which contained 48 nodes and 122 links. Rothia and Cardiobacteriumhad had the highest number of connecting lines. In addition, the negative correlation for Rothia accounted for 64 % of the variance. The negative correlation of Cardiobacterium accounted for 57 %.

5. Discussion

The bidirectional association between diabetes and periodontal disease has been widely described; however, the underlying mechanisms remain unclear. The present research has demonstrated a comprehensive view of the differences in salivary and subgingival microbiome between chronic periodontitis patients and periodontitis patients with T2DM. Additionally, nonsurgical periodontal treatment leads to improved glycemic control and significant reductions of the abundance of most classical periodontal pathogens in periodontitis patients with T2DM.

In the present study, differences were found in the structure and composition of the microbial communities in salivary and subgingival plaques. The abundance of bacterial species in subgingival plaque was higher than that in saliva. In addition, the constituent ratio of periodontal disease-related pathogens in subgingival plaque was also higher than that in saliva. The proportion of Streptococcus, which could prevent other potentially pathogenic bacteria from colonizing, was the highest in the oral saliva [21,22]. Subgingival plaques contained the highest percentages of P. gingivalis. The periodontitis-associated genera Porphyromonas, Prevotella and Fusobacterium accounted for a higher percentage of the microbiota in subgingival plaque than in saliva.

In the present study, subjects with T2DM and chronic periodontitis had significantly lower species abundance and biodiversity in the oral microbiome than did non-T2DM subjects. These results agree with previous studies’ findings [15,23]. Moreover, T2DM subjects had a higher proportion of total clones of Porphyromonas, Fretibacterium, Fusobacterium, Rothia, and cFilifactor than did non-T2DM subjects. These genera are closely associated with chronic periodontitis [24]. Outside of the Red Complex, Filifactor alocis can also predict the severity of chronic periodontitis [25]. In addition, Fusobacterium can play a role in diseases by providing the anaerobic environment that is necessary for the growth of pathogens [26]. T2DM subjects exhibited lower percentages of clones of Comamonadaceae, Comamonas, Actinomyces, Alloprevotella, Oribacterium, Campylobacter, and Parvimonas than did non-T2DM subjects [22].

First, it is be important to consider that elevated glucose concentrations in the saliva and gingival crevicular fluid may stimulate the selective growth of certain oral bacteria at the expense of others [27]. Additionally, mouth dehydration, which is often associated with diabetes, can reduce oral microbial diversity [28]. Third, high glucose concentration may result in salivary acidification, perturbing the oral microbiome [14].

Three months after nonsurgical periodontal therapy, clear changes in the blood glucose levels, periodontal status, and oral flora of periodontitis patients with diabetes have been documented [29]. Given that non-surgical periodontal therapy in patients with chronic periodontitis and diabetes leads to improved periodontal conditions and glycemic control, the dental treatment plays an important role in the managing patients with diabetes. Moreover, the biodiversity of subgingival plaques in diabetic subjects increases significantly after nonsurgical periodontal therapy, and the bacterial community structure becomes more complex. This might be because nonsurgical periodontal treatment changes the high-glucose microenvironment in which oral bacteria live, thus recombining the microflora.

Consistent with previous studies [30], the present study found that the number of classical periodontal pathogens, such as Porphyromonas, Filifactor, and Eubacterium, in the subgingival plaques of diabetic subjects significantly declined following periodontal treatment at the genus level. Interestingly, there was a trend toward an increase in bacteria that are not generally associated with periodontitis (Selenomonas and Actinobacteria), and bacteria that are not generally associated with periodontitis (Streptococcus, Veillonella, Actinomyces, Leptotrichia, and Prevotella), suggesting that the post-treatment community is more complex and less compatible with the survival of pathogenic taxa [21,31]. Therefore, a dynamic balance may exist between probiotic microorganisms and pathogenic bacteria [32]. The transition from periodontitis to healthy conditions might be attributed to a shift in the global balance of the microbiome rather than the disappearance of specific periodontal pathogenic bacteria [33]. Moreover, the increase in probiotic bacteria could be related to the decreased concentration of toxic compounds produced by the reduced abundance of pathogenic microbiota after periodontal treatment [22].

In the present study, the calculation of the alpha diversity displayed a significant increasement in the diversity of subgingival plaques in subjects with diabetes the after periodontal treatment, but the composition of predominant bacterial species did not change significantly. In contrast, treatment did not cause significant differences in the diversity of the salivary microbiome bacteria over time, suggesting resilience to external environmental pressures [31].

Network analysis based on correlations provides useful insights into the microbial connections [34]. The present network analysis showed that subgingival communities were mainly composed of two components in which positive connections prevailed, indicating that the development of periodontitis may result from synergism, rather than antagonism, between various oral disease-associated bacteria. However, the negative connections were mostly between pathogens and host-compatible species, implying that the shift from a health-compatible to a disease-inducing microbiome might be caused by the proportional increase in pathogenic bacteria, rather than by the de novo colonization of oral pathogenic bacteria in previously healthy individuals [20]. Notably, the topology of the post-treatment networks was noticeably different from that of the corresponding pre-treatment networks. Fewer links and a lower level of centralization were observed in CP subjects after treatment. In contrast, the number of links and the density of the networks increased in CP + T2DM subjects, suggesting a more stable community after treatment [22].

The present clinical research, however, might have several limitations. One is the small group size. We analyzed 132 samples from 11 CP + T2DM subjects and 11 CP subjects. Although the numbers of samples analyzed in this study are comparable with other published studies, future studies still need larger cohorts and longer follow-up visit. Another possible limitation could be that the present study only analyze the glucose levels in blood samples, but do not include the lipid panel and inflammatory parameters which are associate with the metabolic comorbidity of T2DM. Thus, further investigations should also analyze biomarkers of the metabolic comorbidity of T2DM.

6. Conclusion

Nonsurgical periodontal treatment effectively improved the periodontal condition and glycemic control in patients with chronic periodontitis and diabetes. Moreover, the microbiota is distinct in saliva and subgingival plaque, and is also different in periodontitis patients with and without companion T2DM [16,20]. Additionally, nonsurgical periodontal therapy can improve the composition of the oral microbiota. Characterizing the differences in the oral microbiota at the onset and after the periodontal therapy in patients with periodontitis and type 2 diabetes can provide novel insights into the adjuvant therapy for T2DM.

Funding statement

This work was supported by the CSA West China Clinical Research Fund (CSA-W2020-01).

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Lan Wang: Writing – original draft, Formal analysis, Conceptualization. Zhi Gao: Writing – review & editing, Validation. Zihan Zhao: Methodology. Xin Shen: Writing – original draft. Jiali Feng: Writing – review & editing, Validation. Jun Xiong: Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.