Dental implants are a popular clinical procedure for the rehabilitation of fully and partially ede ntulous patients. There is long-term evidence that implant-supported dental prostheses represent a predictable treatment for replacing missing teeth. However, several types of complications may arise, which can compromise implant treatment outcome. Peri-implant disease is a growing biological complication, consisting of a progressive loss of supporting bone, associated with microbial biofilm and clinical inflammation. It represents a concern for clinicians and patients, having a negative impact on quality of life. This narrative review aimed at summarize the current knowledge on etiology, epidemiology, risk factors, and pathogenesis of peri-implant disease. It also focused on the diagnostic potential of active matrix metalloproteinase-8 (aMMP-8) in peri-implant sulcular fluid for assessing the status of peri-implant tissues and the risk of developing peri-implantitis. A literature search was conducted in PubMed and Scopus databases using search terms like: peri-implantitis, peri-implant biomarkers, aMMP-8, implant maintenance, risk assessment. Clinical studies, systematic reviews, meta-analysis and consensus papers published up to June 2025 were considered. Finally, based on the main factors involved in the onset and progression of peri-implant disease, a new protocol was conceived for determining the optimal implant maintenance scheduling for individual patients. The Biomarker-Guided Implant Maintenance (BGIM) protocol considers a few key parameters, among which aMMP-8 level, and proposes three categories associated with different levels of risk for peri-implantitis. The higher the risk, the more frequently a patient should undergo professional maintenance, to prevent peri-implant disease, with potential favorable effects on implant longevity. The proposed BGIM protocol, that requires prospective validation, represents a structured and clinically applicable biomarker-driven framework for individualizing implant maintenance scheduling by integrating real-time chairside quantification of aMMP-8 with established patient-related risk factors.
Testori et al. (Tue,) studied this question.