Screening Of Oral Core Microbiome In Periodontitis Patients With Type 2 Diabetes Mellitus And The Establishment Of Risk Prediction Models Of The Diseases

Background and Objective:Periodontitis,a chronic inflammatory disease characterized by the damage of periodontal tissues and alveolar resorption,is the leading cause of tooth loss in Chinese adults.Other than affecting the mastication,periodontitis is also related to systemic health and is the risk factor of cardiovascular diseases,tumors,diabetes,pregnancy complications,rheumatic arthritis,etc.Type 2 diabetes mellitus(T2DM),a metabolic imbalance characterized by chronic high glucose levels,can be caused by multiple factors.The main causes are insufficient insulin release(absolute or relative)and insulin resistance of the target cells.Many studies have proved that periodontitis is a risk factor of T2DM and vice versa.Both of the two diseases are relatively prevalent in China,which significantly affect the oral and systemic health of Chinese people.Now is generally believed that there is a close relationship between periodontitis and T2DM.T2DM can affect the host’s response to inflammation by reducing the host immune level,increasing the synthesis of carbohydrates and lipids,and raising the level of oxidative stress.These result in the T2DM patients with higher susceptibility to periodontitis.The persistence of periodontal inflammation will also increase the serum C-reactive protein,decrease the activity of insulin receptor,and aggravate the insulin resistance,which lead to the fluctuation of blood glucose.Thus,aggressive treatment of periodontitis has a significant impact on blood glucose control.Previousstudies on the mechanism of the interaction between diabetes mellitus and periodontitis have focused on immune inflammations and oxidative stress.Although these studies have already proved the close relationship between periodontitis and T2DM,more evidence is still needed in the interacting mechanism of T2DM and oral microbiome imbalance.Thus,the present study aimed to systematically investigate the populational and functional characteristics of oral microbiome under the periodontitis and high blood glucose status and screen for differential oral microbiomes related to these specific pathogenic conditions.We collected saliva and subgingival plaque samples from T2DM and/or periodontitis-related patients.The samples were tested by 16S rRNA high-throughput sequencing.Next,we analyzed the association of the release of inflammatory factors with clinical parameters and differential microbiomes to explore the possible mechanism of how oral pathogenic microbiomes influence the local periodontal health and even the systemic immunological and inflammatory status.Based on the selected T2DM and periodontitis-related core oral microbiomes,we established and validated random forest models to predict the diseases,aiming to evaluate the possibility to screen high-risk population of periodontitis and T2DM with oral microbiome.Then we compared the differences in the oral microbiome population between samples obtained from different sites to provide a detailed structural pattern of the oral microbiome.In addition,the present study aimed to investigate the impact of T2DM and periodontitis on oral microbiome and establish random forest models to evaluate the possibility of predicting periodontitis and T2DM,especially in screening high-risk diabetic patients in periodontitis patients,with oral microbiome.Lastly,we compared the differencs in the oral microbiome population between samples obtained from different sites to provide a detailed structural pattern of the oral microbiome.Materials and methods:1.Screening of core salivary microbiome in periodontitis wtih T2DM patients and the establishment of disease prediction modelsPatients were randomly recruited from the First Affiliated Hospital of Anhui University and the Oral Hospital affiliated to Anhui Medical University.Enrolled patients were divided into groups after confirmed diagnosis and written informed consent.A questionnaire about background information was filled and clinical parameters were recorded.The study investigated for 5 disease subtypes of periodontitis and T2DM,including healthy controls(Health group),periodontitis patients(P group),T2DM patients(T2DM group),T2DM patients with companion periodontitis(DAP group),and T2DM patients with companion periodontitis who receive long term metformin treatment(Met group).We collected saliva samples from the enrolled patients,extracted DNA and constructed libraries for 16S rRNA sequencing,and obtained the bio-information of the samples’ microbiome.The sequence data were clustered into OTUs at a 97%similarity threshold and aligned to the RDP database(Ribosomal Database Project)for species annotation.QIIME2 was used to calculate the alpha-diversity indices and beta-diversity indices.Alpha-diversity indices included Chao 1 index,Simpson index,Shannon index,observed-OTUs index,and Faith-pd index.Wilcoxon ranked test was used to analyze the difference of alpha-diversity between groups.Beta-diversity indices included PCA analysis and PCoA analysis,calculating the Bray.Wilcoxon ranked test was used to analyze the difference of alpha-diversity between groups.Permutation test was used to select differential microbiome with abundance of>10-4 within group and 3-fold difference in relative abundance between groups at phylum,genus,species,and OTUs levels,respectively.Spearman correlation test was used to analyze the correlation between differential microbiomes of each group and the correlation between species of the microbiome and clinical parameters.Microbiomes with significant differences among groups were sorted by their importance and showed as a dot plot.Ten-fold cross-validation method was used to establish random forest models.ROC curves were plotted to evaluate the prediction performance of the models.External data was used to further validate the accuracy of the models.2.Analysis of the correlation between salivary microbiome and inflammatory factors in T2DM patients with companion periodontitisSaliva samples were collected from Part 1 enrolled patients.The inflammatory factors including IFN-y,interleukin-1β(IL-1β),IL-6,IL-8,IL-23,and tumor necrosis factor-α(TNF-α)were obtained from the supernatant and tested by Millipore multi-factor liquid chip.The results of each group were expressed as average value ±standard deviation.The concentrations of each inflammatory factor were fitst compared among groups of different diseases and shown as a bar chart.Then,spearman correlation test was used to analyze the correlation between microbiome and inflammatory factors.During the correlation analysis,microbiomes with relative abundance of<0.02%and abundance of<20%within group were removed.Results were shown as a heatmap with |cor |≥0.5.3.Screening of subgingival plaque core microbiome in T2DM patients with companion periodontitis and comparison of the salivary core microbiomeSubgingival plaque samples and saliva samples were collected from the same group of outpatients.Subgingival plaque samples were obtained at the deepest possible site and its microbial diversity was tested by 16S rRNA sequencing.Sequencing data were clustered into OTUs and annotated.QIIME2 was used to calculate the alpha-diversity indices and generate box-and-whiskers plots.All the sequence data of the subgingival plaque and saliva were clustered into OTUs at a 97%similarity threshold and analyzed for the alpha-diversity indices(Chao1 index,Simpson index,Shannon index,observed-OTUs index and Faith-pd index)and beta-diversity(PCA analysis and PCoA analysis)between the groups and two sample types.Permutation test was used to screen for differential microbiomes with abundance of>10-4 within group and 3-fold difference in relative abundance between groups at phylum,genus,species,and OTUs levels,respectively.Spearman correlation test was used to analyze the correlation between key salivary microbiomes of each group and the correlation between species of the microbiome and clinical parameters.Microbiomes with significant differences among groups were sorted by their importance and the top 15 differential microbiomes were shown as a dot plot.Ten-fold cross-validation method was used to establish subgingival plaque random forest models for disease prediction.ROC curves were plotted to evaluate the prediction performance of the models.Results:We recruited 133 patients and collected 265 samples,including 133 saliva samples and 132 subgingival plaque samples,in the present study.A total of 19GB sequencing data was generated by 16S rRNA sequencing.After filtering with a maxEE value of 2.5 for quality control,18,087,798 clean reads were obtained,with each covering 68,256 reads on average.After clustering all of the qualified sequences at 97%similarity,we generated 2,707 operational taxonomic units(OTUs).All OTUs were annotated into a total of 32 phyla,64 classes,103 orders,192 families,381 genera,and 394 species.1.Analysis of the diversity of the saliva microbiomeResults of the alpha-diversity indices demonstrated that the microbial diversities of periodontitis patients(P group)and periodontitis patients with T2DM(DAP group)were significantly higher compared to healthy controls(Health group).Patients with T2DM only(T2DM)had no significant difference with the Health group.The Met group who received metformin treatment had relatively lower alpha-diversity indices compared to the P group and the DAP group,while had no significant difference with the Health group.Prevotella(13.3%),Streptococcus(13.2%),Neisseria(9.6%),Fusobacterium(7.6%),Haemophilus(5.6%),Porphyromonas(4.2%)and Treponema(2.4%)are the dominate genus in saliva microbiome.Beta-diversity was significantly different among the groups.In PCoA,the Jaccard and unweighted UniFrac distance showed a remarkable shift in the microbial distribution among the five groups.At the phylum level,seven phyla of common phylum were found,including Actinomycetes,Firmicutes,and Proteobacteria,etc.Thirty-seven common genus and forty-eight species were found at the genus level and species level.In periodontitis patients with T2DM(DAP),results of the differential analysis indicated that other than the known periodontitis-inducing pathogens such as Porphyromonas gingivalis and Tannerella forsythia,the relative abundances of Lactobillus paraplantarum and Acinetobacter nosocomialis increased significantly.After the effective glycemic control(receiving metformin treatment),the relative abundances of Prevotella copri,Prevotella pallens,Rlastonia pickettii,Alloprevotella rava,Phyllobacterium myrsinacearum,Acinetobacter nosocomialis,etc,decreased in periodontitis patients with T2DM.The relative abundances of Streptobacillus moniliformis and Acinetobacter nosocomialis changed under the influence of blood glucose,which were closely related to the fluctuation of blood glucose.The relative abundances of Prevotella copri,Ralstonia pickettii,Alloprevotella rava and Treponema medium in diabetes population correlated with periodontal inflammation.When analyzing the correlations between key salivary microbiomes and clinical parameters,the results demonstrated that different microbiomes were positively associated with clinical parameters(|cor|≥ 0.4,p<0.05).There were 49 species to be demonstrated to have correlations with the clinical indexes of periodontics.Ralstonia insidiosa and Lactobacillus hamsteri were negatively correlated with periodontal clinical indexes,and the others were positively correlated.13 species,such as Acinetobacter nosocomiali,Eubacterium sulci,Peptostreptococcus stomatis and Phascolarctobacterium Succinatutens,had the highest relative abundance in DAP group,and had the positive correlations with periodontal-related clinical parameters.There were only Eubacterium sulci,Lactobacilus mucosae,Lactobacilus paraplantarum,Acinetobacter nosocomialis,Megasphaera Micronuciformis,Ralstonia insidiosa,Lachnoanaerobaculum umeaense and Solobacterium moorei had positive correlations with blood glucose related clinical parameters.The correlation analysis showed that the saliva core microbiome and periodontal clinical parameters had the stronger correlations,and the periodontal healthy status was the main factor influencing the saliva microbial community structure.The selected differential salivary microbiomes were sorted according to their importance.The accuracy of the models in classifying healthy controls and periodontitis patients,periodontitis patients and T2DM patients with companion periodontitis,and periodontitis patients and T2DM patients were 100%,96.3%,and 98.1%,respectively.To further validate the reliability of the results,external data was analyzed by the models and the prediction accuracy of the validation set was 82.2%.2.Analysis of the correlation between salivary microbiome and inflammatory factorsResults of the quantitative analysis with Millipore multi-factor liquid chip demonstrated that the concentrations of IL-1β,IL-6,IL-8,IL-23,and TNF-α in the DAP group and P group were significantly higher compared to the Health group.Compared to the P group,the concentrations of IL-6 and IL-8 were significantly higher in the DAP group.Analysis of the correlation between salivary microbiome and inflammatory factors found that Eubacterium,Prevotella,Alloprevotella,Treponema,Campylobacter,Peptostreptococcus,Lactobacillus were positive correlation with inflammatory factors.Eubacterium sulci,Prevotella melaninogenica and Stomatobaculum longum showed positive correlations with the increase of IL-6 and IL-8,and correlated with the aggravation of periodontal inflammation.Haemophilus pittmaniae was negatively correlated with inflammatory factors in Met group,which was associated with the recovery of local inflammation after the metformin control.3.Analysis of the diversity of the subgingival plaque microbiome and comparison of the subgingival plaque and the salivary microbiomeResults demonstrated that the diversity of the subgingival plaque microbiome increased in both periodontitis patients and diabetic patients.Compared to diabetes only patients,periodontitis patients and periodontitis patients with T2DM showed a more similar alpha-diversity in the subgingival plaque.Glycemic control had beneficial effects on the subgingival plaque of periodontitis patients.After effective glycemic control,the alpha-diversity indices of periodontitis patients with T2DM appeared to significant decrease.The common phylum among groups included Bacteroidetes,Fusobacteria,Proteobacteria,Firmicutes,Actinomycetes,Treponema,etc.In DAP group,the relative abundances of Bacteroidetes(28.7%),Fusobacteria(27.1%),Porphyromonas(11.5%),Proteobacteria(11.5%),Firmicutes(15.6%),Actinomycetes(4%),Treponema(9.7%)were significantly higher.Leptotrichia,Prevotella,Fusobacterium,Treponema,Porphyromonas,Corynebacterium,Neisseria,etc.are the dominate genus in the groups.Thirty-seven common genus of eight common phylum were found in these groups,which included Rothia,Prevotella,Fusobacterium,Bacteroides,Leptotrichia,Porphyromonas,and Treponema.The Bray-Curtis and weighted UniFrac distance showed a remarkable shift in the microbial distribution among the five groups.In the periodontitis patients with T2DM group,the core microbiome of subgingival plaque included Porphyromonas gingivalis,Prevotella copri,Prevotella denticola,Prevotella nigrescens,Alloprevotella tannerae,Eubacterium sulci,Veillonella atypica,Mgasphaera micronuciformis,etc.The relative abundances of these species in subgingival plaque were significantly higher than those in P or T2DM groups.After effective glycemic control(receiving metformin treatment),the relative abundance of Leptotrichia shahii,Prevotella copri,Stomatobaculum longum,Faecalibacterium prausnitzii,Scardovia wiggsiae,etc.in Met group decreased significantly.There was a positive correlation between the species of subgingival plaque(|cor|≥ 0.8).The core subgingival plaque microbiomes were closely related to the periodontal-related clinical parameters,but had nothing to do with the blood glucose levels.Actinomyces gerenoseriae,Streptococcus sanguinis,Capnocytophaga ochracea,Rothia aeria,etc.had negative correlation with periodontal-related clinical parameters.Porphyromonas gingivalis,Treponema denticola,Prevotella denticola,Prevotella nigrescens,Veillonella atypica,Eubacterium sulci,Alloprevotella tannerae and Porphyromonas endodontalis,etc.were positively associated with periodontal-related clinical parameters.The selected differential subgingival plaque microbiomes were sorted according to their importance.The accuracy of the models in classifying healthy controls and periodontitis patients,periodontitis patients and T2DM patients with companion periodontitis,and periodontitis patients and T2DM patients were 100%,97.9%,and 98.3%,respectively.Results of the alpha-diversity indices demonstrated that the subgingival plaque were significantly higher compared with saliva.The Bray-Curtis and weighted UniFrac distance showed a remarkable shift in the microbial distribution between them.In DAP group,saliva and subgingival plaques shared nine phylum and forty-eight genus,including Bacteroidetes,Fusobacteria,Firmicutes,Proteobacteria,Acfinobacteria,Treponema,etc.Compared to the saliva,the relative abundances of anaerobic bacteria such as Leptotrichia,Fusobacterium,Porphyromonas,Treponema,Tannerella,Capnocytophaga,Campylobacter,Aggregatibacter,Selenomonas,etc.were significantly higher in subgingival plaque.In contrast,the relative abundances of facultative anaerobe or microaerophilic bacteria such as Streptococcus dentisani,Gemella haemolysans,Haemophilus parainfluenzae,Prevotella copri,Rothia aeria,Alloprevotella rava,etc.were significantly higher in the saliva.Conclusions:1.Diversity of saliva microbiome was significantly increased in periodontitis with or without T2DM,In addition to well-known main periodontal pathogens,Eubacterium,Acinetobacter,Prevotella,etc.also play an important synergistic role in the changes of oral microbiome structure in patients with periodontitis and T2DM,which increases the risk of periodontal infection.Glycemic control might improve the salivary microbiome structure for patients with both periodontitis and T2DM.Random forest models established based on selected microbiomes could effectively differentiate between patients with periodontitis-and T2DM-related symptoms.2.In patients with periodontitis and T2DM,microbiome structure(such as Eubacterium,Prevotella,Alloprevotella,Treponema,Campylobacter,Peptostreptococcus,Lactobacillus,etc.)is associated with presence of inflammatory factors(IFN-γ,IL-1β,IL-6,L-8 and TNF-α).For a patient with periodontitis,the structure of saliva microbiome may be useful for prognosis.3.Although both periodontitis and T2DM can lead to increase of microbiome diversity of subgingival plaque,periodontitis shows stronger connection.In addition to the known red complex,Prevotella,Leptotrichia,Alloprevotella,Eubacterium,Veillonell,etc.are also key species involved in the structural alteration of subgingival plaque microbiome.The random forest models established based on the selected differential microbiomes from subgingival plaque could effectively differentiate between patients with periodontitis-and T2DM-related symptoms.4.Significant differences were observed between salivary and subgingival plaque microbiome for periodontitis patients with or without T2DM.The species diversity of the subgingival plaque microbiome was higher compared to that of the salivary microbiome.The relative abundances of anaerobic bacteria such as Porphyromonas,Treponema,Alloprevotella,Fusobacterium,Tannerella,Campylobacter,Aggregatibacter,etc.were significantly higher in the subgingival plaque.In contrast,the relative abundance of facultative anaerobe or microaerophilic bacteria such as Streptococcus and Rothia were significantly higher in the saliva.No matter healthy or not,the oral microbiome was site-specific.