Salivary Microbiome and Metabolome Signatures as Predictors of Peri-Implantitis: A Prospective Multi-Omics Cohort Study

Abstract

Peri-implantitis represents an increasingly important complication of implant-supported rehabilitation, yet conventional clinical indicators frequently identify disease only after substantial inflammatory and osseous changes have occurred. This prospective cohort study proposes to determine whether integrated salivary microbiome and metabolome profiles can predict the onset and progression of peri-implantitis before clinically evident tissue destruction. Adults with functionally loaded dental implants and without baseline peri-implantitis will be enrolled across multiple university dental centers and followed for 24 months. Peri-implant probing depth, bleeding and suppuration on probing, plaque accumulation, mucosal recession, and standardized radiographic marginal bone levels will be recorded at baseline and scheduled follow-up visits. Unstimulated saliva and peri-implant sulcular fluid will undergo shotgun metagenomic sequencing and untargeted liquid chromatography–mass spectrometry metabolomic profiling. Microbial diversity, functional pathway composition, virulence-related genes, and metabolite concentrations will be integrated with demographic, behavioral, systemic, prosthetic, and implant-related characteristics. Machine-learning algorithms using nested cross-validation will identify multi-omics signatures associated with incident peri-implantitis and clinically relevant bone loss. Sparse feature-selection methods will be used to derive a parsimonious biomarker panel suitable for subsequent clinical translation. Predictive performance will be assessed using discrimination, calibration, precision-recall analysis, and decision-curve analysis and compared with models based exclusively on conventional clinical risk factors. Temporal network analyses will further investigate whether alterations in microbial functions and metabolic products precede inflammatory deterioration or emerge as consequences of established disease. The study is designed to move peri-implant diagnostics from cross-sectional microbial description toward longitudinal prediction of disease. Identification of reproducible biological signatures preceding peri-implant breakdown could enable personalized surveillance and targeted preventive care for susceptible implant patients. In addition, characterizing coordinated microbiome–metabolome changes may clarify biological mechanisms connecting microbial dysbiosis with host inflammatory responses and peri-implant bone destruction.