Periodontal diagnosis: towards a molecular approach via mouthwash

Periodontitis, which affects approximately 50% of the global adult population, is driven by a complex dysbiotic transition of oral microbial communities. Although the 2018 AAP/EFP classification allows for rigorous clinical stratification, the microbiological signature associated with each stage remains insufficiently documented, particularly in Asian populations. The major clinical challenge lies in the search for diagnostic tools that are less invasive than conventional subgingival sampling, which is often tedious and localised.

This pilot cross-sectional study aims to characterise the oral microbiome profiles of 74 Korean adults divided into three groups: healthy controls, stage I–II periodontitis, and stage III–IV periodontitis. The main objective is to validate the use of a mouthwash as a non-invasive sampling method providing a representative snapshot of the overall microbial reservoir (including the mucosa and supragingival plaque).

The authors test the hypothesis that changes in diversity and the presence of specific taxa correlate precisely with disease severity. By identifying a "core microbiome" and emerging pathobionts via 16S rRNA sequencing, this research lays the foundation for a molecular diagnostic tool capable of assessing periodontal status from a simple vigorous mouth rinse.

A protocol based on microbiome analysis via oral rinse

This prospective cross-sectional pilot study characterises the oral microbiome of 74 Korean adults. The clinical team divides the participants into three distinct groups according to the 2018 AAP/EFP classification:

• Healthy controls (n = 24): probing depth (PD) ≤ 3 mm and bleeding on probing (BOP) < 10%.
• Periodontitis Stages I–II (n = 12): initial to moderate forms.
• Periodontitis Stages III–IV (n = 38): severe to advanced forms.

Diagnosis is based on an artificial intelligence-assisted radiographic analysis to measure the crestal bone level. Estimated clinical attachment loss (eCAL) is calculated by subtracting the histological biologic width (2.04 mm) from the bone measurement. To standardise the sampling, each patient performs a supervised brushing before using 10 mL of mouth rinse solution (COOL SENSE, Docsmedi Co., Ltd.).

Microbiological analysis is based on genomic DNA extraction and sequencing of the V3–V4 hypervariable region of the 16S rRNA gene. The sequences are processed via the EzBioCloud pipeline with a 97% similarity threshold. Statistical processing uses PERMANOVA models adjusted for covariates (age, sex, smoking) and the MaAsLin2 tool for differential abundance (significance threshold q < 0.05).

Microbial diversity: a restructuring of the ecosystem

Alpha diversity analysis reveals that advanced disease stages (Stage III–IV) exhibit significantly higher evenness than healthy or Stage I–II groups. This trend is confirmed by the Shannon and Simpson indices, as well as Pielou's evenness. Contrary to popular belief, this shift reflects a more balanced distribution of pathogenic species rather than a mere increase in species richness.

Regarding beta diversity, the PERMANOVA analysis shows a clear and statistically significant separation of the microbial communities among the three groups (p = 0.001). This result remains robust even after sequential adjustment for confounding factors such as age, sex and smoking.

Taxonomic biomarkers and signatures of dysbiosis

Differential abundance analysis (MaAsLin2) identified 14 genera significantly associated with periodontal status. Twelve of them are enriched in stages III–IV, including classic and emerging pathogens. Conversely, two genera are characteristic of periodontal health.

Analysis of the core microbiome (Core Microbiome)

The study identified 40 genera universally present in all samples. However, a specific marker stood out: the genus Anaeroglobus was detected exclusively in the Stage III–IV group with a prevalence of 100%. This exclusivity makes it a strong candidate for the molecular diagnosis of severe forms.

Clinically and radiographically, the use of an artificial intelligence platform for the analysis of panoramic radiographs allowed for the precise quantification of crestal bone loss. The estimated clinical attachment level (eCAL), calculated from these radiographic measurements (by subtracting the biological width of 2.04 mm), served as a solid basis for the correlation between the observed tissue destruction and the microbial profiles identified by the mouthwash.

Clinical analysis: Mouthwash, a mirror of dysbiosis

This study demonstrates that the progression towards severe stages of periodontitis (III-IV) is not characterised by a simple bacterial proliferation, but by a significant increase in Shannon diversity and Pielou's regularity (evenness). Clinically, this suggests a profound shift in the ecosystem where emerging pathobionts, such as Filifactor and Fretibacterium, establish themselves alongside traditional red complexes like Tannerella and Treponema. The most striking result remains the identification of Anaeroglobus, exclusively detected in 100% of stage III-IV patients, making it a prime biomarker candidate for severity diagnosis.

The use of oral rinse samples, although less localised than subgingival plaque sampling, validates its role here as an overall "thermometer" of periodontal health. The persistence of Rothia and Kingella in the healthy groups highlights the importance of nitrate reducers in maintaining an environment compatible with health. However, the inherent limitations of this cross-sectional design involving 74 patients do not allow the establishment of a strict causality between microbiome evolution and clinical attachment loss measured by radiographic AI.

For the practitioner, these data confirm that molecular diagnostics via a simple mouthwash could soon complement the traditional clinical examination. Early identification of a dysbiotic signature, even before irreversible bone loss, would pave the way for targeted and more personalised preventive periodontology.

Summary of results

This cross-sectional study conducted on 74 patients demonstrates that the oral microbiome undergoes a major restructuring during the transition to stages III-IV of periodontitis, marked by an increased species homogeneity (evenness). In addition to classic pathogens, the genus Anaeroglobus shows a 100% prevalence in severe forms, while the emerging pathobionts Filifactor and Fretibacterium confirm an advanced dysbiotic signature, detectable by a simple oral rinse.

In practical terms, for the practitioner:

• Non-invasive diagnosis: Oral rinse sampling is clinically validated to discriminate disease stages (p=0.001); it offers a comprehensive view of the microbial reservoir, simplifying screening compared to invasive subgingival sampling.
• New severity markers: No longer limit yourself to the red complex; the identification of Filifactor or Anaeroglobus signals a shift towards severe periodontal stages, necessitating more aggressive therapy upon their detection.
• Success indicators: The predominance of Rothia and Kingella is associated with periodontal health. Their return after treatment constitutes an objective biomarker of flora stabilisation and the success of your maintenance protocols.

Technical glossary of the study

AAP/EFP 2018 Classification: Periodontal diagnostic framework structuring pathologies into Stages (I to IV) based on severity, management complexity and the extent of clinical attachment loss.

16S rRNA sequencing: Metagenomic technique targeting the hypervariable regions (V3–V4) of the 16S ribosomal RNA gene to identify the bacterial diversity and taxonomic composition of a complex sample.

Alpha diversity: Statistical measure (Shannon, Simpson and Pielou indices) assessing the richness and evenness of species distribution within a single bacterial sample.

Beta Diversity: Analysis of the variation in the composition of microbial communities between different patient groups, enabling the identification of a distinct microbiological signature according to periodontal status.

Pathobionts: Commensal microorganisms which, under the influence of environmental dysbiosis, proliferate and become pathogenic, such as the genera Filifactor and Fretibacterium identified in this study.

Core Microbiome: Set of microbial taxa present in 100% of individuals within a specific group, serving as a universal biological marker for a given clinical condition.

MaAsLin2: Bioinformatics tool for multivariable association discovery used to isolate taxa specifically linked to the disease by adjusting for confounding variables such as age, sex and smoking.

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