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. 2025 Jun 24;52(9):1314–1326. doi: 10.1111/jcpe.14192

Characteristics of Subgingival Plaque Microbiome in Japanese Older Adults With Healthy Gingiva

Tomoki Akatsu 1,✉, Hatsumi Souno 1, Akihiko Fujii 1, Yoshihiko Minegishi 1, Noriyasu Ota 1, Yoshihisa Yamashita 2
PMCID: PMC12377948  PMID: 40555474

ABSTRACT

Aim

To elucidate the characteristics of the subgingival plaque microbiome in older adults without gingival inflammation.

Materials and Methods

Subgingival plaque from 180 participants was collected and analysed using 16S rRNA sequencing. Based on the clinical parameters at the sampling sites, participants were categorised as healthy (gingival index [GI] = 0, maximum probing pocket depth [PPDmax] ≤ 2.0 and gingival recession [GR] = 0) or non‐healthy (GI > 0, or PPDmax > 2.0 or GR > 0). Each group was further stratified by age into younger (< 65 years) and older (≥ 65 years) subgroups. We performed diversity and linear discriminant effect size (LEfSe) analyses to elucidate microbiome characteristics of healthy older adults.

Results

We observed differences in α‐diversity and β‐diversity between younger and older individuals only in the healthy group. Healthy older individuals showed a lower α‐diversity index, indicating a healthy‐like profile shift and also a significantly greater difference in β‐diversity from the non‐healthy group than the healthy younger subgroup. LEfSe analysis indicated that six amplicon sequence variants (ASVs), such as Rothia dentocariosa , Neisseria perflava and Actinomyces sp. HMT‐448, were predominant in the healthy older subgroup.

Conclusion

Maintaining lower α‐diversity, with an abundance of R. dentocariosa and N. perflava, which are possible nitrate‐reducing bacteria, may contribute to lifelong healthy gingiva by preventing microbial dysbiosis.

Keywords: commensal bacteria, dysbiosis, microbiome, nitrate‐reducing bacteria, periodontal disease

1. Introduction

Periodontal disease is a chronic inflammatory disease characterised by the destruction of periodontal tissues, primarily induced by periodontitis‐associated bacterial infection (Frencken et al. 2017; Pihlstrom et al. 2005). Previous studies have identified the ‘Red complex’ bacteria, including Porphyromonas gingivalis ( P. gingivalis ), Tannerella forsythia ( T. forsythia ) and Treponema denticola ( T. denticola ), as critical contributors to periodontal disease development (Socransky et al. 1998). However, recent research suggests that periodontal disease progression is less about specific pathogens and more about dysbiosis, which is a microbial imbalance that influences disease progression (Darveau 2010; Hajishengallis et al. 2023; Lamont and Hajishengallis 2015; Nath and Raveendran 2013). This imbalance highlights the importance of the entire microbial community, particularly the subgingival plaque microbiome, in influencing periodontal health. Therefore, maintaining a healthy subgingival plaque microbiome is crucial for sustaining a healthy oral environment. Extensive research has focused on the microbiome in patients with periodontitis; however, its characteristics in healthy individuals are poorly understood (Abusleme et al. 2013, 2021; Hong et al. 2015; Kirst et al. 2015; Li et al. 2014).

Notably, findings from several Japanese cohort studies have revealed variations in oral microbiome composition among healthy individuals, linking diverse bacterial communities with superior periodontal health (Lenartova et al. 2021; Saito et al. 2020; Takeshita et al. 2016). These studies suggest that maintaining a balanced oral microbiome is crucial for the prevention of periodontal disease. Additionally, these large‐scale studies indicate that oral microbiome dysbiosis occurs even in healthy individuals and is involved in the early stages of periodontal disease. However, the specific features of the subgingival plaque microbiome, which are mostly associated with early‐stage periodontal disease in healthy older adults, are poorly characterised.

Given the association between microbiome alterations, periodontal disease progression and ageing, it has been hypothesized that older adults capable of maintaining healthy gingiva possess a distinctive beneficial subgingival plaque microbiome. This hypothesis is particularly relevant because of age‐related vulnerabilities, including immune dysfunction, which increase susceptibility to periodontal and other infectious diseases (Ebersole et al. 2016; Hajishengallis 2014). While previous studies have documented age‐related changes in the oral microbiome composition (Takeshita et al. 2016; Wu et al. 2020; Xu et al. 2015), comprehensive investigations specifically examining the subgingival microbiome of healthy older adults compared with younger individuals with equivalent periodontal health status remain limited. Interestingly, some researchers have reported that the subgingival microbiome of periodontally healthy older adults is broadly similar to that of younger individuals, with no major differences in the prevalence of key bacteria (Feres et al. 2016). However, these observations were based on limited data, and the precise microbial signatures that might distinguish age‐related changes in subgingival microbiota of healthy individuals remain to be elucidated.

Therefore, in this study, we aimed to address this knowledge gap by elucidating the microbial features associated with periodontal health in an ageing population, with particular focus on the subgingival microbiome. We hypothesised that older adults with healthy gingiva harbour a distinctive microbial community profile—defined by specific diversity patterns and microbial compositions—that may serve as a protective mechanism against periodontal disease, despite age‐related reductions in immune function. By identifying these critical microbial components and communities associated with gingival health in older adults, our findings could inform the development of targeted strategies to promote oral homeostasis and prevent periodontal disease in ageing populations.

2. Materials and Methods

2.1. Study Participants and Sample Collection

The Institutional Review Board of Kao Corporation approved this study (approval number: T208‐190315). In total, 240 Japanese participants living in Tokyo and aged 5–79 years were recruited between April and May 2019. Informed consent was obtained from all participants, as described previously (Kobayashi et al. 2022). Participants were excluded if they met any of the following criteria: (1) diagnosed with any serious disease (e.g., hepatic or renal dysfunction, cardiovascular disease, fractures, muscle tears, etc.); (2) receiving medication for cardiovascular or metabolic diseases (e.g., diabetes); (3) currently pregnant, possibly pregnant or within 6 months postpartum; (4) experiencing itching or redness upon application of alcohol to the skin; (5) presence of wounds at the observation sites (oral cavity or palm); (6) undergoing orthodontic treatment; (7) wearing full dentures; or (8) experiencing poor physical condition (e.g., a cold or foot/waist pain) on the day of the study.

We measured dental clinical indicators for only 180 of the 240 participants (90 males and 90 females) aged ≥ 20 years because of the invasive nature of the procedures. The clinical assessments included the gingival index (GI) (Löe 1967), bleeding on probing (BOP), periodontal pocket depth (PPD) and the greatest gingival recession (GR; measured in millimetres from the cemento‐enamel junction to the gingival crest) by a single examiner (H.S.), who is a trained dental hygienist with 33 years of clinical experience. Subsequently, subgingival plaque samples were collected from the lingual side of the lower left fourth to seventh molars using a sterile scaler. These clinical parameters of the subgingival plaque sampling sites were used in the analysis to investigate the relationship between periodontal health and the subgingival plaque microbiome. The collected samples were stored at −80°C until further processing.

2.2. 16S rRNA Gene Sequencing

We extracted DNA from subgingival plaque samples using the DNeasy PowerSoil Kit (QIAGEN) with an additional enzymatic lysis step. Specifically, each sample was treated with 180 μL of enzyme lysis buffer (20 mM Tris–HCl, pH 8.0, 2 mM EDTA, 1.2% Triton X‐100 and 20 mg/mL lysozyme) and incubated at 37°C for 50 min, followed by the remaining steps according to the manufacturer's protocol. The subgingival plaque microbiome was analysed through high‐throughput sequencing of the 16S rRNA gene region on the MiSeq (Illumina). We selected the V1–V2 regions based on previous studies indicating their effectiveness for oral microbiome analysis (Na et al. 2023). The V1–V2 regions were amplified with primers 27Fmod (5′‐TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGAGRGTTTGATYMTGGCTCAG‐3′) and 338R (5′‐GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGTGCTGCCTCCCGTAGGAGT‐3′). Amplicon PCR (polymerase chain reaction) and index PCR were both performed using the KAPA HiFi HotStart Ready Mix (KAPA Biosystems), and the resulting PCR products were purified using AMPure XP beads (Beckman Coulter). All procedures were conducted according to the manufacturer's protocol. The final library was pooled, diluted to 8 pM with 15% PhiX (Illumina) and sequenced using the MiSeq Reagent Kit v3 (Illumina) for 300 bp paired‐end reads.

2.3. Sequencing Data Processing and Statistical Analysis

Raw 16S rRNA gene sequences were processed using QIIME2 (version 2021.2) (Bolyen et al. 2019) with the DADA2 pipeline (Callahan et al. 2016) for amplicon sequence variant (ASV) identification and error correction. The taxonomic classification was performed using the expanded Human Oral Microbiome Database (eHOMD) using a scikit‐learn naïve Bayes classifier (Escapa et al. 2018). Primary microbial community data analysis was conducted using the phyloseq package (McMurdie and Holmes 2013). Differences in microbiome composition were evaluated using linear discriminant analysis effect size (LEfSe) analysis (Segata et al. 2011). Detailed information on these methods is provided in Supporting Information.

Variables were analysed using the Wilcoxon rank‐sum exact test to assess differences between stratified groups and Welch's t‐test for clinical indicators. Smoking history between groups was analysed using Pearson's chi‐squared or Fisher's exact test. Beta‐diversity distance matrices were compared across groups using permutation analysis of variance (PERMANOVA) with 9999 permutations. For comparisons among three groups, pairwise comparisons were adjusted using Bonferroni correction.

3. Results

3.1. Clinical Characteristics of the Study Population

A rarefaction analysis performed to determine an appropriate sequencing depth while maximising sample retention (Figure S1) indicated a minimum cut‐off of 8427 reads for subsequent analyses. Accordingly, seven participants who fell below this threshold were excluded. Additionally, 11 participants with GR > 1—indicative of advanced periodontal disease—were excluded, bringing the total number of excluded participants to 17 and leaving a final sample of 163 (Figure 1). These 163 participants were subsequently classified according to their mean GI, maximum PPD and maximum GR at the sampling sites, and were categorised as healthy (H: GI = 0, PPDmax ≤ 2.0 and GR = 0) or non‐healthy (nH; GI > 0, PPDmax > 2.0 or GR > 0) (Figure 1, Table 1). Table 1 presents each group's clinical parameters. Statistical analysis showed significant differences in the mean GI (p < 0.001), BOP (p = 0.008) and PPD (p < 0.001) between the two groups. Although the nH group had inflammation scores suggestive of early‐stage periodontal disease, pockets were shallower in this group than those typically observed in generalised periodontitis, despite the classification.

FIGURE 1.

FIGURE 1

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Flowchart of participant selection criteria and stratification. This flowchart outlines the selection process and stratification criteria for participants in the study. In total, 180 participants were enrolled in the study. Samples with low sequencing quality and those with advanced gingival recession (GR) were excluded from the analysis, resulting in a final cohort of 163 individuals. Participants were further stratified based on clinical parameters and age for subgroup comparisons in subsequent analyses.

TABLE 1.

Clinical characteristics of the study participants.

Healthy, N = 98 a Non‐healthy, N = 65 a Difference b 95% CI b p b
Age 48.02 ± 16.55 50.80 ± 15.11 −2.8 −7.7 to 2.2 0.3
Sex 0.26 −0.04 to 0.59
Female 54 (55.1%) 27 (41.5%)
Male 44 (44.9%) 38 (58.5%)
Mean GI 0.00 ± 0.00 0.67 ± 0.42 −0.67 −0.78 to −0.57 < 0.001
Mean BOP 0.00 ± 0.00 0.11 ± 0.33 −0.11 −0.19 to −0.03 0.008
Mean PPD 1.84 ± 0.28 2.20 ± 0.48 −0.36 −0.49 to −0.23 < 0.001
Max. GR −0.36 −0.68 to −0.05
0 98 (100.0%) 61 (93.8%)
1 0 (0.0%) 4 (6.2%)
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Abbreviations: BOP, bleeding on probing; CI, confidence interval; GI, gingival index; GR, gingival recession; PPD, periodontal pocket depth; SD, standard deviation.

a

Mean ± SD; n (%).

b

Welch two‐sample t‐test.

3.2. Comparative Analysis of Subgingival Plaque Microbiome in Healthy and Non‐Healthy Individuals

Initially, we analysed Chao1, Shannon index and Fisher's alpha to evaluate α‐diversity (Figure 2A–C). In the nH group, a significant increase in all α‐diversity metrics was observed (Chao1 [p = 0.033], Shannon index [p < 0.001] and Fisher's alpha [p = 0.025]), compared with that in the H group. The β‐diversity analysis, which was visualised through a non‐metric multi‐dimensional scaling plot based on Bray–Curtis dissimilarity (Figure 2D) and UniFrac distance (Figure 2E), revealed a significant difference in microbial communities between the H and nH groups. Further analysis of the bacterial abundance revealed distinct compositions between the two groups. The major taxa at the class and genus levels are described using stacked bar charts (Figure 2F,G). In the nH group, a higher prevalence of Fusobacteria, including the genera Fusobacterium and Leptotrichia, was observed. Conversely, the H group exhibited a greater abundance of Actinobacteria and β‐proteobacteria, comprising genera such as Neisseria and Rothia (Figure 2F,G).

FIGURE 2.

FIGURE 2

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Overview of microbial components between healthy (H) and non‐healthy (nH) groups. Chao1 (A), Shannon index (B) and Fisher's alpha (C) are shown to compare the α‐diversity between H and nH groups. NMDS visualisations of β‐diversity analysis using Bray–Curtis dissimilarity (D) and UniFrac distance (E) are shown for each group. The ellipses represent the 90% confidence intervals of the data distribution within each group. Differences in β‐diversity were evaluated using permutational multivariate analysis of variance (PERMANOVA), with R 2 and p‐values shown in the panel. Comparative profiles of the major microbial class (F) and genera (G) are also shown. Significant differences in α‐diversity indices were evaluated using the Wilcoxon rank‐sum exact test. *p < 0.05; **p < 0.01 and ***p < 0.001 indicate statistical significance.

Subsequently, LEfSe analysis (Segata et al. 2011) was performed to evaluate group differences in bacterial composition at the genus and ASV levels. The analysis identified 31 genera and 67 ASVs that differed between the H and nH groups (Figure 3A,B). The nH individuals in this study had a relatively shallow PPD; however, the LEfSe analysis revealed a significantly higher abundance of periodontitis‐associated genera, including Fusobacterium, Saccharibacteria (TM7) [G‐1], Prevotella and Treponema, compared with the H group (Figure 3A). In contrast, the microbiome of healthy individuals predominantly comprised non‐pathogenic commensal bacteria such as Neisseria, Actinomyces and Lautropia. Notably, ASV analysis detected P. gingivalis , T. forsythia and T. denticola (the Red complex species) as characteristic ASVs of non‐healthy individuals (Figure 3B).

FIGURE 3.

FIGURE 3

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Characterisation of microbial composition in Healthy and Non‐Healthy participants using linear discriminant analysis effect size analysis. Linear discriminant analysis effect size (LEfSe) plots show taxa with significantly different abundances between healthy and non‐healthy participants at the genus level (A) and ASV level (B). Genera with an average relative abundance of ≥ 0.1% and amplicon sequence variants (ASVs) with an average relative abundance of ≥ 0.1% were included in the analysis. The LEfSe analysis, performed using the lefser package, applied the Wilcoxon rank‐sum test with a significance threshold of p < 0.05. Linear discriminant analysis (LDA) scores were used to rank the differential features, with a cut‐off value of 2.0.

Smoking history did not differ significantly between the H and nH groups (Table S1). An LEfSe analysis comparing current smoker and non‐current smoker groups revealed that Prevotella intermedia was more abundant in the current smoker group, while Rothia dentocariosa was more prevalent in the non‐current smoker group (Figure S2).

3.3. Healthy Ageing in the Subgingival Plaque Microbiome

The H and nH groups were further stratified into two age categories (younger < 65 years and older ≥ 65 years) to examine differences in the subgingival plaque microbiome under comparable oral health statuses, rather than focusing on age‐related changes alone. In the nH group, older participants showed significantly higher PPD than their younger counterparts (Table S2). In contrast, although the H group showed a significant age difference, there were no significant differences in GI, BOP or PPD (Table 2).

TABLE 2.

Clinical characteristics of the Healthy participants.

Healthy group, N = 98 Difference b 95% CI b p b
Younger, N = 76 a Older, N = 22 a
Age 41.54 ± 12.63 70.41 ± 4.25 −29.0 −32 to −25 < 0.001
Sex 0.10 −0.37 to 0.58
Female 41 (53.9%) 13 (59.1%)
Male 35 (46.1%) 9 (40.9%)
Mean GI 0.00 ± 0.00 0.00 ± 0.00 0.00
Mean BOP 0.00 ± 0.00 0.00 ± 0.00 0.00
Mean PPD 1.84 ± 0.26 1.85 ± 0.33 −0.01 −0.17 to 0.15 0.9
Max GR 0.00 −0.47 to 0.47
1 76 (100.0%) 22 (100.0%)
2 0 (0.0%) 0 (0.0%)
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Abbreviations: BOP, bleeding on probing; CI, confidence interval; GI, gingival index; GR, gingival recession; PPD, periodontal pocket depth; SD, standard deviation.

a

Mean ± SD; n (%).

b

Welch two‐sample t‐test.

We evaluated the α‐diversity indices, including Chao1, Shannon index and Fisher's alpha, for younger and older individuals within the H and nH groups. Remarkably, within the H group, these α‐diversity indices were significantly lower in older individuals than in their younger counterparts; however, age‐related differences were not observed in the nH group (Figure 4A–C).

FIGURE 4.

FIGURE 4

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Overview of microbial components between older and younger groups in healthy and non‐healthy individuals. Chao1 (A), Shannon index (B) and Fisher's alpha (C) are shown to compare the α‐diversity between older and younger individuals within each group (healthy [H] and non‐healthy [nH]). NMDS plot of β‐diversity based on Bray–Curtis dissimilarity (D) and UniFrac distance (E) in subgingival plaque samples from HO (blue), HY (red) and nH (green) individuals. The ellipses represent the 90% confidence intervals of the data distribution within each group. Permutational multivariate analysis of variance (PERMANOVA) was used to assess β‐diversity differences between age groups, with p‐values shown in the panels (Bray–Curtis, F; UniFrac, G). Pairwise comparisons were adjusted using the Bonferroni correction. Boxplot comparing the distance of the plots of Healthy‐older and younger participants from the plot centroid of the nH (Bray–Curtis, H; UniFrac, I). Significant differences in α‐diversity indices were evaluated using the Wilcoxon rank‐sum exact test. *p < 0.05; **p < 0.01 and ***p < 0.001 indicate statistical significance. HO, healthy older; HY, healthy younger.

β‐Diversity analysis based on Bray–Curtis dissimilarity and UniFrac distance displayed a marked distinction between the H and nH groups (Figure 4D,E). PERMANOVA indicated statistically significant differences among the three groups (healthy younger [HY], healthy older [HO] and nH), suggesting that each group may harbour distinct microbial compositions (Figure 4F,G). Notably, the plots corresponding to the HO group appeared to be located further from those of the nH group compared with the plot position of the HY group. To quantitatively assess this observation, we compared the distances of individual plots in the H group from the nH group between younger and older healthy participants (Figure 4H,I). This analysis confirmed that the microbial community composition of the HO group showed significantly higher dissimilarity to the nH group than that of the HY group, regardless of whether Bray–Curtis dissimilarity or UniFrac distance metrics were used. In the nH group, age did not influence microbial differences (Figures S3 and S4).

3.4. Characterisation of Microbial Composition in Healthy Older Adults

We performed LEfSe analysis to clarify the characteristic species in the HO group, who maintained good gingival health in older age. The analysis revealed significant differences, identifying 10 genera and 22 ASVs between the HY and HO groups (Figure 5A,B). At the genus level, several taxa were characteristic of the HY group, including Leptotrichia, Corynebacterium, Saccharibacteria (TM7) [G‐1] and Porphyromonas, as determined by the LDA score (Figure 5A). In contrast, the ASV analysis revealed 22 ASVs with distinct relative abundances between the groups. Notably, six ASVs (Rothia dentocariosa, Actinomyces sp. HMT 448, Neisseria perflava , Veillonella parvula , Actinomyces sp. HMT 169 and Streptococcus oralis subsp. oralis) were identified as characteristics of the HO group (Figure 5B).

FIGURE 5.

FIGURE 5

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Characterisation of microbial composition in healthy older and healthy younger participants using linear discriminant analysis effect size analysis. Linear discriminant analysis effect size (LEfSe) plots show taxa with significantly different abundances between healthy older and healthy younger participants at the genus level (A) and ASV level (B). Genera with an average relative abundance of ≥ 0.1% and ASVs with an average relative abundance of ≥ 0.1% were included in the analysis. The LEfSe analysis applied the Wilcoxon rank‐sum test with a significance threshold of p < 0.05. Linear discriminant analysis (LDA) scores were used to rank the differential features, with a cut‐off value of 2.0.

To evaluate the effects of smoking, we first compared the smoking history proportions between the HY and HO groups, which showed no significant differences (Table S3). We then conducted LEfSe analysis within the non‐current smoker population. The HO group showed a predominance of R. dentocariosa , Actinomyces sp. HMT 448 and N. perflava_asv3, exhibiting similar patterns at both genus and ASV levels as those observed in the initial cohort analysis (Figures 5B and S5).

4. Discussion

In this study, we investigated the relationship between the subgingival plaque microbiome and periodontal health, particularly in healthy older adults. Our findings provide new insights that contribute to the understanding of age‐related variations in the subgingival microbiome and their potential importance in maintaining oral health. Our results revealed the dynamic nature of the subgingival microbiome, demonstrating its variation between healthy and non‐healthy individuals, as well as within the H group across different age populations.

We categorised the participants into H (inflammation‐free gingiva) and nH (signs of early‐stage periodontal disease) groups based on their gingival health (Figure 1). Despite only slight clinical differences, α‐diversity indices and β‐diversity differed significantly from those in the H group (Table 1, Figure 2A–E). These findings indicate that subgingival microbial alterations can arise early, before pronounced pocket formation. One reason for these alterations may be a more anaerobic environment and the presence of periodontopathic bacteria, given that the non‐healthy microbiome displayed higher richness (Figure 2A) and a higher abundance of periodontitis‐associated genera and ASVs (Figure 3). However, considering the relatively low PPD in the nH group, the observed shifts in microbial community composition might be more closely linked to inflammatory mediators than to increased anaerobic conditions. Inflammatory conditions promote an increased relative abundance of proteolytic taxa with a concurrent decrease in saccharolytic taxa (Belibasakis et al. 2023; Marsh 2003). In our results, proteolytic taxa such as Fusobacterium and Treponema, as well as species P. gingivalis, T. forsythia and T. denticola, were predominant in non‐healthy participants with gingival inflammation (Figure 3), consistent with previous studies (Arredondo et al. 2023; Iniesta et al. 2023; Winning et al. 2023). Therefore, a distinct local environment—not solely anaerobiosis—may be shaping the subgingival microbiome. These findings suggest that changes in the subgingival microbiome composition occur early in the disease process before the development of deep anaerobic environments in the periodontal pockets, potentially contributing to disease progression.

The lower α‐diversity in healthy older adults initially appears to contradict conventional findings. While periodontal disease typically exhibits increased microbial diversity (Abusleme et al. 2013; Griffen et al. 2012), Chen et al. suggested that the moderately low diversity in healthy oral environments may reflect a more stable community structure associated with oral health maintenance (Chen et al. 2018). Previous studies have shown age‐related changes in salivary microbiomes (Lira‐Junior et al. 2018), with Takeshita et al. identifying distinct community types: one with higher Prevotella, Veillonella, Actinomyces and Rothia in older adults, and another with more Neisseria, Haemophilus and Porphyromonas in younger individuals (Takeshita et al. 2016). While some reviews suggest that the subgingival microbiome remains similar across age groups in healthy individuals (Feres et al. 2016), our findings reveal more nuanced differences, with β‐diversity varying significantly between healthy younger and older individuals (Figure 4). Previous studies consistently report reduced Haemophilus in older adults regardless of oral health status (Wu et al. 2020; Xu et al. 2015). These age‐related microbiome alterations possibly reflect broader physiological shifts associated with ageing (David et al. 2014; Takeshita et al. 2016). Our study is among the first to specifically characterise these differences in the subgingival microbiome of clinically healthy individuals, suggesting that these compositional differences may have significant implications for periodontal health maintenance in ageing populations.

Ageing and the associated decline in immune function increase the prevalence of periodontal disease (Albandar 2005; Billings et al. 2018), suggesting that older adults might be more susceptible to subgingival microbiome pathogenicity. We examined how ageing affects the subgingival microbiome in a healthy population.

Interestingly, older adults with healthy gingiva showed significantly lower α‐diversity than younger individuals, despite the lack of significant differences in PPD (Figure 4A–C, Table 2). This age‐related difference in α‐diversity was observed only in the H group, with no significant differences among the nH group. Considering that periodontitis patients typically exhibit increased microbial diversity (Abusleme et al. 2013; Arredondo et al. 2023; Iniesta et al. 2023; Wei et al. 2019), the lower α‐diversity in older adults with healthy gingiva might reflect a microbiome more compatible with maintaining oral health. Consistent with these findings, β‐diversity analysis revealed distinct age‐related shifts in microbial composition even among healthy individuals, suggesting that older adults with healthy gingiva may sustain a subgingival microbiome further removed from dysbiosis. These results underscore the importance of examining age‐related changes—even among individuals who appear clinically healthy—to better understand how certain microbial profiles might protect against periodontal disease.

LEfSe analysis identified four ASVs, R. dentocariosa , N. perflava , Veillonella parvula and Streptococcus oralis subsp. oralis among the predominant six ASVs in the HO group as nitrate‐reducing bacteria (NRB) (Goh et al. 2019; Hyde et al. 2014) (Figure 5B). Based on previous reports identifying Actinomyces species as NRB, Actinomyces sp. HMT 448 and Actinomyces sp. HMT 169, which were also identified in the HO group, may also function as NRB. In contrast, only 1 ASV, Corynebacterium matruchotii , among the 16 predominant ASVs was detected as NRB in the younger population.

Nitrate‐reducing bacteria use the nitrate–nitrite–nitric oxide pathway to produce nitric oxide (NO), a bioactive molecule with potent antimicrobial properties against anaerobic periodontal pathogens including P. gingivalis and T. denticola (Backlund et al. 2014; Rosier, Buetas, et al. 2020, Rosier et al. 2022; Rosier, Moya‐Gonzalvez, et al. 2020). This mechanism highlights the significant impact of local nitrate‐reducing activity in sustaining a healthy subgingival microbiome predominantly consisting of commensal bacteria (Rosier et al. 2022, 2024; Simpson et al. 2024). Recent studies have shown that individuals with periodontitis have a significantly reduced capacity for nitrate reduction and lower abundance of NRB, while periodontal treatment or dietary nitrate supplementation restores NRB levels and NO generation (Jockel‐Schneider et al. 2016; Mazurel et al. 2023). Notably, Simpson et al. recently reported that successful periodontal treatment leads to a longitudinal increase in nitrite‐producing bacteria, further supporting the mechanistic link between NRB abundance and periodontal health (Simpson et al. 2024). Mazurel et al. demonstrated that the addition of nitrate to periodontitis‐associated microbial communities decreased biofilm mass and reduced the abundance of periodontitis‐associated species, leading to a significant decrease in the dysbiosis index (Mazurel et al. 2023). These findings collectively highlight a crucial relationship between local nitrate metabolism and periodontal homeostasis. Research on oral microbiome interventions has begun to explore the influence of dietary components such as nitrate‐rich beetroot or lettuce juice on enhancing NRB populations, including Rothia and Neisseria (Alhulaefi et al. 2024; Jockel‐Schneider et al. 2021; Velmurugan et al. 2016). Our findings that healthy older adults exhibited a distinct microbial profile marked by increased abundance of multiple NRB were not observed in the HY group, suggesting that NRB may represent a microbial signature of periodontal resilience in ageing. We propose that these NRB could serve not only as biomarkers of periodontal health but also as potential targets for microbiome‐based preventive strategies in older populations.

The relationship between smoking and oral microbiome has been extensively reported in numerous studies (Bizzarro et al. 2013; Haffajee and Socransky 2001; Kumar et al. 2011; Mason et al. 2015). Smokers generally exhibit decreased commensal bacteria and increased anaerobic microbiome, including periodontal pathogens. Notably, Haffajee et al. observed a higher abundance of F. nucleatum subsp. vincentii and P. intermedia in smokers, consistent with our current observations (Figure S2). Further, despite the lack of significant differences in smoking history between the groups, analyses within the non‐current smoker population still revealed the characteristic microbial differences in healthy aged individuals (Table S3, Figure S5). These results suggest that our findings regarding the distinctive subgingival microbiome in healthy older adults likely represent age‐specific characteristics independent of smoking history, providing further support for the potential protective role of the identified microbial communities in maintaining periodontal health during ageing.

Our study has several limitations. First, the cross‐sectional study design did not permit us to establish a causal relationship between specific microbes and long‐term periodontal health. Second, the study population consisted exclusively of Japanese individuals from a single geographic region, limiting the generalisability of our findings. Additionally, we selected the V1–V2 region for 16S rRNA gene sequencing (Na et al. 2023), which has limitations in distinguishing closely related species (Abusleme et al. 2021). The exclusive use of LEfSe for microbiome data analysis is another limitation, as it may not fully account for the compositional nature of microbiome data (Gloor et al. 2017; Morton et al. 2019).

Further longitudinal studies with racially, ethnically and geographically diverse populations and larger sample sizes are needed to validate these findings. Furthermore, mechanistic studies are necessary to reveal the precise biological pathways through which NRB exert protective effects on the oral microbiome. Understanding these underlying mechanisms is crucial for developing targeted microbiome‐based interventions to promote oral health across the lifespan. These investigations can further reveal the potential of new microbiome‐targeted strategies to prevent and manage early‐stage periodontal diseases.

5. Conclusion

Our findings identified critical microbial characteristics essential for maintaining periodontal health in ageing individuals. Key findings indicate that older adults with healthy gingiva possess a distinct subgingival microbiome characterised by lower microbial diversity compared with their younger counterparts, greater compositional distance from disease‐associated microbiomes and significant enrichment of potential nitrate‐reducing bacteria, particularly R. dentocariosa and N. perflava . This microbial profile likely serves as a protective mechanism against dysbiosis, even in clinically healthy individuals. Maintaining these specific bacterial communities may be essential for preserving periodontal health, despite age‐related immunity changes. Future longitudinal studies across diverse populations should investigate how these bacteria contribute to oral health, informing the development of microbiome‐targeted interventions to prevent periodontal disease in ageing populations.

Author Contributions

T.A. contributed to data analysis and manuscript drafting. Y.M. and N.O. contributed to study conception. T.A. and Y.Y. contributed to microbial study design. H.S. contributed by measuring dental clinical index scores and collecting plaque samples. All authors contributed to data interpretation. Y.Y. contributed to critical revision of the manuscript. All authors read and approved the final manuscript.

Ethics Statement

This study was approved by the Ethics Committee of Kao Corporation (approval number: T208‐190315) and was performed following the principles of the Declaration of Helsinki. All participants provided written informed consent after receiving sufficient explanation regarding the purpose and content of the study.

Conflicts of Interest

T.A., H.S., A.F., Y.M. and N.O. are with Kao Corporation. Y.Y. provided academic advice on microbial methodology.

Supporting information

Data S1. Supporting Information.

JCPE-52-1314-s001.docx (3.6MB, docx)

Acknowledgements

We would especially like to thank all participants involved in measuring dental scores and sampling biomaterial for this study. Without them, this study would not have been possible. Further, we thank Sawako Kawano, Atsuko Hayase and Takuya Mori for their support during the study. We would also like to thank Editage (www.editage.jp) for English language editing.

Akatsu, T. , Souno H., Fujii A., Minegishi Y., Ota N., and Yamashita Y.. 2025. “Characteristics of Subgingival Plaque Microbiome in Japanese Older Adults With Healthy Gingiva.” Journal of Clinical Periodontology 52, no. 9: 1314–1326. 10.1111/jcpe.14192.

Funding: This study was performed and funded by Kao Corporation. There was no external funding for this study; all costs were covered by in‐house resources.

Data Availability Statement

The raw 16S rRNA gene sequencing data generated in this study are publicly available in the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA1171414. The analysis pipeline and code used for data processing and analysis, and the other datasets generated or analyzed during this study, are available from the corresponding author upon reasonable request.

References

  1. Abusleme, L. , Dupuy A. K., Dutzan N., et al. 2013. “The Subgingival Microbiome in Health and Periodontitis and Its Relationship With Community Biomass and Inflammation.” ISME Journal 7, no. 5: 1016–1025. 10.1038/ismej.2012.174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abusleme, L. , Hoare A., Hong B.‐Y., and Diaz P. I.. 2021. “Microbial Signatures of Health, Gingivitis, and Periodontitis.” Periodontology 2000 86, no. 1: 57–78. 10.1111/prd.12362. [DOI] [PubMed] [Google Scholar]
  3. Albandar, J. M. 2005. “Epidemiology and Risk Factors of Periodontal Diseases.” Dental Clinics of North America 49, no. 3: 517–532. 10.1016/j.cden.2005.03.003. [DOI] [PubMed] [Google Scholar]
  4. Alhulaefi, S. S. , Watson A. W., Ramsay S. E., et al. 2024. “Effects of Dietary Nitrate Supplementation on Oral Health and Associated Markers of Systemic Health: A Systematic Review.” Critical Reviews in Food Science and Nutrition 65, no. 14: 1–16. 10.1080/10408398.2024.2351168. [DOI] [PubMed] [Google Scholar]
  5. Arredondo, A. , Àlvarez G., Isabal S., et al. 2023. “Comparative 16S rRNA Gene Sequencing Study of Subgingival Microbiota of Healthy Subjects and Patients With Periodontitis From Four Different Countries.” Journal of Clinical Periodontology 50, no. 9: 1176–1187. 10.1111/jcpe.13827. [DOI] [PubMed] [Google Scholar]
  6. Backlund, C. J. , Sergesketter A. R., Offenbacher S., and Schoenfisch M. H.. 2014. “Antibacterial Efficacy of Exogenous Nitric Oxide on Periodontal Pathogens.” Journal of Dental Research 93, no. 11: 1089–1094. 10.1177/0022034514529974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Belibasakis, G. N. , Belstrøm D., Eick S., Gursoy U. K., Johansson A., and Könönen E.. 2023. “Periodontal Microbiology and Microbial Etiology of Periodontal Diseases: Historical Concepts and Contemporary Perspectives.” Periodontology 2000. 10.1111/prd.12473. [DOI] [PubMed] [Google Scholar]
  8. Billings, M. , Holtfreter B., Papapanou P. N., Mitnik G. L., Kocher T., and Dye B. A.. 2018. “Age‐Dependent Distribution of Periodontitis in Two Countries: Findings From NHANES 2009 to 2014 and SHIP‐TREND 2008 to 2012.” Journal of Clinical Periodontology 45, no. Suppl 20: S130–S148. 10.1111/jcpe.12944. [DOI] [PubMed] [Google Scholar]
  9. Bizzarro, S. , Loos B. G., Laine M. L., Crielaard W., and Zaura E.. 2013. “Subgingival Microbiome in Smokers and Non‐Smokers in Periodontitis: An Exploratory Study Using Traditional Targeted Techniques and a Next‐Generation Sequencing.” Journal of Clinical Periodontology 40, no. 5: 483–492. 10.1111/jcpe.12087. [DOI] [PubMed] [Google Scholar]
  10. Bolyen, E. , Rideout J. R., Dillon M. R., et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2.” Nature Biotechnology 37, no. 8: 852–857. 10.1038/s41587-019-0209-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Callahan, B. J. , McMurdie P. J., Rosen M. J., Han A. W., Johnson A. J. A., and Holmes S. P.. 2016. “DADA2: High‐Resolution Sample Inference From Illumina Amplicon Data.” Nature Methods 13, no. 7: 581–583. 10.1038/nmeth.3869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chen, C. , Hemme C., Beleno J., et al. 2018. “Oral Microbiota of Periodontal Health and Disease and Their Changes After Nonsurgical Periodontal Therapy.” ISME Journal 12, no. 5: 1210–1224. 10.1038/s41396-017-0037-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Darveau, R. P. 2010. “Periodontitis: A Polymicrobial Disruption of Host Homeostasis.” Nature Reviews. Microbiology 8, no. 7: 481–490. 10.1038/nrmicro2337. [DOI] [PubMed] [Google Scholar]
  14. David, L. A. , Materna A. C., Friedman J., et al. 2014. “Host Lifestyle Affects Human Microbiota on Daily Timescales.” Genome Biology 15, no. 7: R89. 10.1186/gb-2014-15-7-r89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ebersole, J. L. , Graves C. L., Gonzalez O. A., et al. 2016. “Aging, Inflammation, Immunity and Periodontal Disease.” Periodontology 2000 72, no. 1: 54–75. 10.1111/prd.12135. [DOI] [PubMed] [Google Scholar]
  16. Escapa, I. F. , Chen T., Huang Y., Gajare P., Dewhirst F. E., and Lemon K. P.. 2018. “New Insights Into Human Nostril Microbiome From the Expanded Human Oral Microbiome Database (eHOMD): A Resource for the Microbiome of the Human Aerodigestive Tract.” MSystems 3, no. 6. 10.1128/mSystems.00187-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Feres, M. , Teles F., Teles R., Figueiredo L. C., and Faveri M.. 2016. “The Subgingival Periodontal Microbiota of the Aging Mouth.” Periodontology 2000 72, no. 1: 30–53. 10.1111/prd.12136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Frencken, J. E. , Sharma P., Stenhouse L., Green D., Laverty D., and Dietrich T.. 2017. “Global Epidemiology of Dental Caries and Severe Periodontitis – A Comprehensive Review.” Journal of Clinical Periodontology 44, no. Suppl 18: S94–S105. 10.1111/jcpe.12677. [DOI] [PubMed] [Google Scholar]
  19. Gloor, G. B. , Macklaim J. M., Pawlowsky‐Glahn V., and Egozcue J. J.. 2017. “Microbiome Datasets Are Compositional: And This Is Not Optional.” Frontiers in Microbiology 8: 2224. 10.3389/fmicb.2017.02224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Goh, C. E. , Trinh P., Colombo P. C., et al. 2019. “Association Between Nitrate‐Reducing Oral Bacteria and Cardiometabolic Outcomes: Results From ORIGINS.” Journal of the American Heart Association 8, no. 23: e013324. 10.1161/JAHA.119.013324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Griffen, A. L. , Beall C. J., Campbell J. H., et al. 2012. “Distinct and Complex Bacterial Profiles in Human Periodontitis and Health Revealed by 16S Pyrosequencing.” ISME Journal 6, no. 6: 1176–1185. 10.1038/ismej.2011.191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Haffajee, A. D. , and Socransky S. S.. 2001. “Relationship of Cigarette Smoking to the Subgingival Microbiota.” Journal of Clinical Periodontology 28, no. 5: 377–388. 10.1034/j.1600-051x.2001.028005377.x. [DOI] [PubMed] [Google Scholar]
  23. Hajishengallis, G. 2014. “Aging and Its Impact on Innate Immunity and Inflammation: Implications for Periodontitis.” Journal of Oral Biosciences 56, no. 1: 30–37. 10.1016/j.job.2013.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hajishengallis, G. , Lamont R. J., and Koo H.. 2023. “Oral Polymicrobial Communities: Assembly, Function, and Impact on Diseases.” Cell Host & Microbe 31, no. 4: 528–538. 10.1016/j.chom.2023.02.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hong, B.‐Y. , Furtado Araujo M. V., Strausbaugh L. D., Terzi E., Ioannidou E., and Diaz P. I.. 2015. “Microbiome Profiles in Periodontitis in Relation to Host and Disease Characteristics.” PLoS One 10, no. 5: e0127077. 10.1371/journal.pone.0127077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hyde, E. R. , Andrade F., Vaksman Z., et al. 2014. “Metagenomic Analysis of Nitrate‐Reducing Bacteria in the Oral Cavity: Implications for Nitric Oxide Homeostasis.” PLoS One 9, no. 3: e88645. 10.1371/journal.pone.0088645. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Iniesta, M. , Chamorro C., Ambrosio N., Marín M. J., Sanz M., and Herrera D.. 2023. “Subgingival Microbiome in Periodontal Health, Gingivitis and Different Stages of Periodontitis.” Journal of Clinical Periodontology 50, no. 7: 905–920. 10.1111/jcpe.13793. [DOI] [PubMed] [Google Scholar]
  28. Jockel‐Schneider, Y. , Goßner S. K., Petersen N., et al. 2016. “Stimulation of the Nitrate‐Nitrite‐NO‐Metabolism by Repeated Lettuce Juice Consumption Decreases Gingival Inflammation in Periodontal Recall Patients: A Randomized, Double‐Blinded, Placebo‐Controlled Clinical Trial.” Journal of Clinical Periodontology 43, no. 7: 603–608. 10.1111/jcpe.12542. [DOI] [PubMed] [Google Scholar]
  29. Jockel‐Schneider, Y. , Schlagenhauf U., Stölzel P., et al. 2021. “Nitrate‐Rich Diet Alters the Composition of the Oral Microbiota in Periodontal Recall Patients.” Journal of Periodontology 92, no. 11: 1536–1545. 10.1002/JPER.20-0778. [DOI] [PubMed] [Google Scholar]
  30. Kirst, M. E. , Li E. C., Alfant B., et al. 2015. “Dysbiosis and Alterations in Predicted Functions of the Subgingival Microbiome in Chronic Periodontitis.” Applied and Environmental Microbiology 81, no. 2: 783–793. 10.1128/AEM.02712-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kobayashi, K. , Shono C., Mori T., et al. 2022. “Protein‐Bound Sialic Acid in Saliva Contributes Directly to Salivary Anti‐Influenza Virus Activity.” Scientific Reports 12, no. 1: 1–9. 10.1038/s41598-022-10559-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kumar, P. S. , Matthews C. R., Joshi V., de Jager M., and Aspiras M.. 2011. “Tobacco Smoking Affects Bacterial Acquisition and Colonization in Oral Biofilms.” Infection and Immunity 79, no. 11: 4730–4738. 10.1128/IAI.05371-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lamont, R. J. , and Hajishengallis G.. 2015. “Polymicrobial Synergy and Dysbiosis in Inflammatory Disease.” Trends in Molecular Medicine 21, no. 3: 172–183. 10.1016/j.molmed.2014.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lenartova, M. , Tesinska B., Janatova T., et al. 2021. “The Oral Microbiome in Periodontal Health.” Frontiers in Cellular and Infection Microbiology 11: 629723. 10.3389/fcimb.2021.629723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Li, Y. , He J., He Z., et al. 2014. “Phylogenetic and Functional Gene Structure Shifts of the Oral Microbiomes in Periodontitis Patients.” ISME Journal 8, no. 9: 1879–1891. 10.1038/ismej.2014.28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lira‐Junior, R. , Åkerman S., Klinge B., Boström E. A., and Gustafsson A.. 2018. “Salivary Microbial Profiles in Relation to Age, Periodontal, and Systemic Diseases.” PLoS One 13, no. 3: e0189374. 10.1371/journal.pone.0189374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Löe, H. 1967. “The Gingival Index, the Plaque Index and the Retention Index Systems.” Journal of Periodontology 38, no. 6: 610–616. 10.1902/jop.1967.38.6.610. [DOI] [PubMed] [Google Scholar]
  38. Marsh, P. D. 2003. “Are Dental Diseases Examples of Ecological Catastrophes?” Microbiology (Reading, England) 149, no. Pt 2: 279–294. 10.1099/mic.0.26082-0. [DOI] [PubMed] [Google Scholar]
  39. Mason, M. R. , Preshaw P. M., Nagaraja H. N., Dabdoub S. M., Rahman A., and Kumar P. S.. 2015. “The Subgingival Microbiome of Clinically Healthy Current and Never Smokers.” ISME Journal 9, no. 1: 268–272. 10.1038/ismej.2014.114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Mazurel, D. , Carda‐Diéguez M., Langenburg T., et al. 2023. “Nitrate and a Nitrate‐Reducing Rothia aeria Strain as Potential Prebiotic or Synbiotic Treatments for Periodontitis.” npj Biofilms and Microbiomes 9, no. 1: 1–12. 10.1038/s41522-023-00406-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. McMurdie, P. J. , and Holmes S.. 2013. “Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PLoS One 8, no. 4: e61217. 10.1371/journal.pone.0061217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Morton, J. T. , Aksenov A. A., Nothias L. F., et al. 2019. “Learning Representations of Microbe‐Metabolite Interactions.” Nature Methods 16, no. 12: 1306–1314. 10.1038/s41592-019-0616-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Na, H. S. , Song Y., Yu Y., and Chung J.. 2023. “Comparative Analysis of Primers Used for 16S rRNA Gene Sequencing in Oral Microbiome Studies.” Methods and Protocols 6, no. 4: 71. 10.3390/mps6040071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Nath, S. G. , and Raveendran R.. 2013. “Microbial Dysbiosis in Periodontitis.” Journal of Indian Society of Periodontology 17, no. 4: 543–545. 10.4103/0972-124X.118334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Pihlstrom, B. L. , Michalowicz B. S., and Johnson N. W.. 2005. “Periodontal Diseases.” Lancet 366, no. 9499: 1809–1820. 10.1016/S0140-6736(05)67728-8. [DOI] [PubMed] [Google Scholar]
  46. Rosier, B. T. , Buetas E., Moya‐Gonzalvez E. M., Artacho A., and Mira A.. 2020. “Nitrate as a Potential Prebiotic for the Oral Microbiome.” Scientific Reports 10, no. 1: 12895. 10.1038/s41598-020-69931-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Rosier, B. T. , Takahashi N., Zaura E., et al. 2022. “The Importance of Nitrate Reduction for Oral Health.” Journal of Dental Research 101, no. 8: 887–897. 10.1177/00220345221080982. [DOI] [PubMed] [Google Scholar]
  48. Rosier, B. T. , Johnston W., Carda‐Diéguez M., et al. 2024. “Nitrate Reduction Capacity of the Oral Microbiota Is Impaired in Periodontitis: Potential Implications for Systemic Nitric Oxide Availability.” International Journal of Oral Science 16, no. 1: 1–10. 10.1038/s41368-023-00266-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Rosier, B. T. , Moya‐Gonzalvez E. M., Corell‐Escuin P., and Mira A.. 2020. “Isolation and Characterization of Nitrate‐Reducing Bacteria as Potential Probiotics for Oral and Systemic Health.” Frontiers in Microbiology 11: 555465. 10.3389/fmicb.2020.555465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Saito, S. , Aoki Y., Tamahara T., et al. 2020. “Oral Microbiome Analysis in Prospective Genome Cohort Studies of the Tohoku Medical Megabank Project.” Frontiers in Cellular and Infection Microbiology 10: 604596. 10.3389/fcimb.2020.604596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Segata, N. , Izard J., Waldron L., et al. 2011. “Metagenomic Biomarker Discovery and Explanation.” Genome Biology 12, no. 6: R60. 10.1186/gb-2011-12-6-r60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Simpson, A. , Johnston W., Carda‐Diéguez M., et al. 2024. “Periodontal Treatment Causes a Longitudinal Increase in Nitrite‐Producing Bacteria.” Molecular Oral Microbiology 39, no. 6: 491–506. 10.1111/omi.12479. [DOI] [PubMed] [Google Scholar]
  53. Socransky, S. S. , Haffajee A. D., Cugini M. A., Smith C., and Kent R. L. Jr. 1998. “Microbial Complexes in Subgingival Plaque.” Journal of Clinical Periodontology 25, no. 2: 134–144. 10.1111/j.1600-051x.1998.tb02419.x. [DOI] [PubMed] [Google Scholar]
  54. Takeshita, T. , Kageyama S., Furuta M., et al. 2016. “Bacterial Diversity in Saliva and Oral Health‐Related Conditions: The Hisayama Study.” Scientific Reports 6, no. 1: 1–11. 10.1038/srep22164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Velmurugan, S. , Gan J. M., Rathod K. S., et al. 2016. “Dietary Nitrate Improves Vascular Function in Patients With Hypercholesterolemia: A Randomized, Double‐Blind, Placebo‐Controlled Study.” American Journal of Clinical Nutrition 103, no. 1: 25–38. 10.3945/ajcn.115.116244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Wei, Y. , Shi M., Zhen M., et al. 2019. “Comparison of Subgingival and Buccal Mucosa Microbiome in Chronic and Aggressive Periodontitis: A Pilot Study.” Frontiers in Cellular and Infection Microbiology 9: 53. 10.3389/fcimb.2019.00053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Winning, L. , Moran G., McClory M., et al. 2023. “Subgingival Microbial Diversity and Respiratory Decline: A Cross‐Sectional Study.” Journal of Clinical Periodontology 50, no. 7: 921–931. 10.1111/jcpe.13819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Wu, L. , Zeng T., Deligios M., et al. 2020. “Age‐Related Variation of Bacterial and Fungal Communities in Different Body Habitats Across the Young, Elderly, and Centenarians in Sardinia.” MSphere 5, no. 1. 10.1128/mSphere.00558-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Xu, X. , He J., Xue J., et al. 2015. “Oral Cavity Contains Distinct Niches With Dynamic Microbial Communities.” Environmental Microbiology 17, no. 3: 699–710. 10.1111/1462-2920.12502. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supporting Information.

JCPE-52-1314-s001.docx (3.6MB, docx)

Data Availability Statement

The raw 16S rRNA gene sequencing data generated in this study are publicly available in the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA1171414. The analysis pipeline and code used for data processing and analysis, and the other datasets generated or analyzed during this study, are available from the corresponding author upon reasonable request.

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