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Comprehensive Analysis of Risk Factors for Periodontitis Focusing on the Saliva Microbiome and Polymorphism

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International Journal of Environmental Research and Public Health Article Comprehensive Analysis of Risk Factors for Periodontitis Focusing on the Saliva Microbiome and Polymorphism Naoki Toyama 1,* , Daisuke Ekuni 1 , Daisuke Matsui 2, Teruhide Koyama 2 , Masahiro Nakatochi 3, Yukihide Momozawa 4, Michiaki Kubo 4 and Manabu Morita 1 1 Department of Preventive Dentistry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2-5-1 Shikata-cho, Kita-ku, Okayama 700-8558, Japan; [email protected] (D.E.); [email protected] (M.M.) 2 Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan; [email protected] (D.M.); [email protected] (T.K.) 3 Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan; [email protected] 4 Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City 230-0045, Japan; [email protected] (Y.M.); [email protected] (M.K.) * Correspondence: [email protected]; Tel.: +81-86-235-6712 Abstract: Few studies have exhaustively assessed relationships among polymorphisms, the micro- biome, and periodontitis. The objective of the present study was to assess associations simultaneously among polymorphisms, the microbiome, and periodontitis. We used propensity score matching with a 1:1 ratio to select subjects, and then 22 individuals (mean age ± standard deviation, 60.7 ± 9.9 years) Citation: Toyama, N.; Ekuni, D.; were analyzed. After saliva collection, V3-4 regions of the 16S rRNA gene were sequenced to in- Matsui, D.; Koyama, T.; Nakatochi, vestigate microbiome composition, alpha diversity (Shannon index, Simpson index, Chao1, and M.; Momozawa, Y.; Kubo, M.; Morita, abundance-based coverage estimator) and beta diversity using principal coordinate analysis (PCoA) M. Comprehensive Analysis of Risk based on weighted and unweighted UniFrac distances. A total of 51 single-nucleotide polymor- Factors for Periodontitis Focusing on the Saliva Microbiome and phisms (SNPs) related to periodontitis were identified. The frequencies of SNPs were collected Polymorphism. Int. J. Environ. Res. from Genome-Wide Association Study data. The PCoA of unweighted UniFrac distance showed a Public Health 2021, 18, 6430. significant difference between periodontitis and control groups (p < 0.05). There were no significant https://doi.org/10.3390/ differences in alpha diversity and PCoA of weighted UniFrac distance (p > 0.05). Two families ijerph18126430 (Lactobacillaceae and Desulfobulbaceae) and one species (Porphyromonas gingivalis) were observed only in the periodontitis group. No SNPs showed significant expression. These results suggest Academic Editor: Florence Carrouel that periodontitis was related to the presence of P. gingivalis and the families Lactobacillaceae and Desulfobulbaceae but not SNPs. Received: 20 May 2021 Accepted: 10 June 2021 Keywords: periodontitis; microbiota; single-nucleotide polymorphisms Published: 14 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional affil- iations. Periodontitis, which induces alveolar bone loss, is inflammation of periodontal tissue caused by bacterial infection. The World Health Organization reported that periodontitis is one of the main causes of tooth loss and can subsequently worsen an individual’s quality of life due to tooth loss [1]. Periodontitis affects some diseases, such as diabetes mellitus, kidney disease, premature birth, aspiration pneumonia, and arteriosclerosis [2]. Therefore, Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. prevention of periodontitis is important to maintain both systemic and oral health. This article is an open access article Periodontitis is caused by a biofilm, which is a collection of any types of bacteria [3,4]. distributed under the terms and A biofilm of 1 mg includes over 1 billion bacteria. Some bacteria attach on teeth and make conditions of the Creative Commons microcolonies. Those colonies gather and make biofilms. In biofilms, bacteria interact and Attribution (CC BY) license (https:// obtain some benefits, including a broader habitat range, efficient metabolism, increased creativecommons.org/licenses/by/ tolerance, and enhanced virulence [5]. The concept of microbiome analysis in periodontal 4.0/). science is to investigate the diversity of communities of bacteria. Int. J. Environ. Res. Public Health 2021, 18, 6430. https://doi.org/10.3390/ijerph18126430 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 6430 2 of 12 It is important to control risk factors for periodontitis to prevent disease. Previous studies reported several risk factors, including smoking, diabetes mellitus, stress, and obesity [2]. Recently, studies reported the microbiome [6] and polymorphisms [7] as risk factors for periodontitis. Polymorphisms are the most common types of genetic variation among individuals [8]. They occur once in 500–1000 nucleotides. Most polymorphisms have no effect on periodontal health, but some polymorphisms are associated with risks of disease. Munz et al. showed that MTND1P5 (re16870060-G) and SHISA9 (rs729876-T) were associated with the risk of periodontitis [7]. Dentists have been struggling to prevent worsening periodontitis. Because the mecha- nism of worsening periodontitis is a host–parasite interaction, we have to pay attention to both the oral microbiome and host factors. However, few studies showed associa- tions between the microbiome (parasite) and polymorphisms (host) at the same time. We hypothesized that there are associations between polymorphisms, the microbiome, and periodontitis. The objective of the present study was to assess associations simultaneously between polymorphisms, the microbiome, and periodontitis. 2. Materials and Methods 2.1. Study Population The present study was a cross-sectional study including individuals who were enrolled in the Japan Multi-Institutional Collaborative Cohort Study (J-MICC study) second survey in the Kyoto area from 2013 to 2017. Participants underwent oral examinations, were measured for height and weight, provided saliva and blood samples, and answered self- reported questionnaires. The inclusion criterion was participants who provided saliva samples. The exclusion criteria were participants who had no teeth and could not be evaluated for the community periodontal index (CPI) and who had CPI = 1–3. 2.2. Ethical Procedures and Informed Consent The study was approved by the Institutional Ethics Committee of Kyoto Prefectural University of Medicine (approval number: RBMR-E-36-8, in 2013) and was conducted in accordance with the principles of the Declaration of Helsinki. All participants provided written, informed consent before participation. 2.3. Oral Examinations One general dentist checked dental status using a dental mirror and explorer under artificial light. Periodontal status was assessed using the CPI (World Health Organization, 4th edition). The CPI was measured for 10 teeth (maxilla: right first and second molars, right central incisor, left first and second molars; mandible: right first and second molars, left central incisor, left first and second molars). A CPI of 0 was defined as no periodontitis (control group), and a CPI of 4 was defined as periodontitis (periodontitis group). 2.4. Questionnaires A self-administered questionnaire was used to determine whether participants had risk factors for periodontitis. Based on the Japanese Association of Periodontology Clinical Practice Guideline for the Periodontal Treatment, 2015 [2], we checked for risk factors such as age, sex, stress, smoking, and diabetes mellitus as follows: 1. Have you felt stress during the past year? (Severe/Moderate/Mild/None) 2. Are you a smoker? (Yes/Past/No) 3. Do you take at least one medication per week to lower blood sugar levels? (Yes/No) We combined “Severe” and “Moderate” responses as positive awareness, and “Mild” and “None” responses as negative awareness to evaluate stress. Participants who took medicines to lower blood sugar levels were defined as having diabetes mellitus. Int. J. Environ. Res. Public Health 2021, 18, 6430 3 of 12 2.5. Salivary Microbiome Analysis To investigate microbiome composition, saliva samples were collected. Saliva was collected by passive drool through a 1 inch straw into a vial. Approximately 1.0 mL of saliva was collected into a 10 mL centrifuge tube (Salivette®, Sarstedt AG & Co., Nümbrecht, Germany) from all examinees. Saliva samples in centrifuge tubes were stored at −20 ◦C until assayed. To prevent contamination, sterilized tubes were used for saliva collection. DNA sequencing was performed based on the 16S metagenomics sequencing li- brary preparation protocol [9] at the Oral Microbiome Center (Taniguchi Dental Clinic, Kagawa, Japan). First, DNA was extracted from saliva. Saliva was suspended using lysis buffer and crushed for 3 min. After centrifugation, DNA was extracted using Gen- Find 2.0 (Beckman Coulter Inc., Brea, CA, USA). During the first PCR, the V3 and V4 regions of the 16S rRNA gene were amplified using primers 341F (forward primer: 50- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGNNNCCTACGGGNGGCWGCAG-30) and 806R (reverse primer: 50-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGN NNGACTACHVGGGTATCTAATCC-30), using Kapa
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