How shotgun metagenomics can improve early diagnosis of peri-implant diseases

Current diagnostic methods for peri-implantitis—primarily based on bleeding on probing (BoP), suppuration, and probing pocket depth (PPD)—often detect disease only after substantial tissue damage has already occurred.

The limitations of these traditional diagnostics are well known to clinicians. While BoP and PPD measurements are essential clinical tools, their sensitivity to early disease detection is modest at best. By the time these parameters become clearly positive, the disease process is often well established. This diagnostic gap has real consequences—both clinical and economic—as late detection frequently necessitates complex (and costly) surgical interventions.

Microbiome analysis offers new diagnostic possibilities

Our cross-sectional study applied shotgun metagenomics technology to analyse subgingival plaque microbiome samples from 158 sites across 102 patients, comparing healthy implants, mucositis, and peri-implantitis cases. Using MetaPhlAn 4, a computational tool that can identify microorganisms at the species and strain level, we catalogued the complete microbial communities present at each site.

The results revealed 447 different bacterial species in the peri-implant environment—considerably more diversity than had been suggested in previous studies using traditional methods. Perhaps more surprisingly, 150 of these species (34%) were previously uncharacterized microorganisms that current databases cannot yet name. Despite being "unknown”, many of these species showed strong associations with specific disease states.

Each clinical condition displayed its own characteristic microbiome pattern. Healthy sites maintained diverse microbial communities that varied considerably between individuals. In contrast, peri-implantitis sites showed much more consistent microbiome profiles across different patients, suggesting that disease drives convergence toward specific pathogenic communities. Mucositis appeared as an intermediate state, sharing features with both healthy sites and disease sites.

Machine learning enhances diagnostic precision

To translate these complex microbiome profiles into clinically useful information, we employed machine-learning algorithms—specifically random-forest classifiers—to identify which microbial patterns best predicted each clinical condition. The accuracy of these models was good to excellent, with area-under-the-curve (AUC) values between 0.78 and 0.96 depending on the specific comparison.

These accuracy levels are encouraging, particularly for distinguishing peri-implantitis from healthy sites (AUC 0.96). The models also showed meaningful correlations with traditional clinical parameters, validating the concept that the microbial signatures reflect genuine biological processes rather than statistical artifacts.

One particularly interesting finding involved Fusobacterium nucleatum, a species long associated with periodontal disease. Our high-resolution analysis revealed that different subspecies of F. nucleatum play distinct roles—some associated with health, others with mucositis, and with specific strains strongly linked to peri-implantitis. This subspecies-level discrimination would be impossible with conventional diagnostic methods.

Practical applications in clinical settings

How might this technology translate to everyday practice? The most straightforward application would be risk assessment for implant patients. During routine maintenance visits, microbiome analysis could identify patients who are developing pathogenic communities before clinical signs appear. This early warning system could trigger preventive interventions—such as  more frequent professional debridement, targeted antimicrobial therapy, or closer monitoring.

Treatment monitoring represents another promising application. Rather than relying solely on clinical parameters to assess treatment response, microbiome analysis could provide objective evidence of whether therapeutic interventions are successfully shifting the microbial community back toward health. This could help clinicians make more informed decisions about treatment duration and intensity.

The sampling process itself is straightforward, similar to current microbiological sampling methods; samples can be collected during routine examinations using sterile curettes, although proper handling protocols are essential for obtaining reliable results.

Comparing approaches: shotgun metagenomics versus 16S sequencing

Many clinicians are familiar with 16S rRNA sequencing, which has been the standard molecular approach for microbiome studies. While 16S analysis is valuable, shotgun metagenomics offers several advantages for clinical applications. Most importantly, it provides species and strain-level identification rather than just genus-level information. This matters because, as our F. nucleatum findings demonstrate, different strains of the same species can have opposing roles in health and disease.

Shotgun sequencing also captures the entire microbial community—bacteria, viruses, fungi—and their functional genes. This comprehensive view enables detection of antibiotic resistance genes and virulence factors that could inform treatment selection.

Challenges and considerations for implementation

Despite promising results, several hurdles remain before this technology becomes routine in dental practice. Cost is a key consideration because shotgun sequencing is more expensive than 16S sequencing—although prices are declining sharply as the technology improves. However, if early detection prevents even a fraction of complex peri-implantitis cases, the economic argument in favour of shotgun sequencing becomes more favourable.

Standardization presents another challenge. Sampling protocols and analysis pipelines must be rigorously standardized to ensure reproducibility across different settings. This will require coordinated efforts between research groups and professional organizations.

Interpretation of results also requires careful consideration. While our models showed strong predictive accuracy in the research setting, real-world performance may vary. Clinicians will need training to understand and act appropriately on microbiome reports, integrating this new information with traditional clinical diagnosis.