Due to the limited efficacy of its treatment modalities, there is a stringent need to improve the prevention and early diagnosis of peri-implantitis. In fact, to date clinical and radiographic tools are not able to discern which patients are going to develop peri-implantitis and, among the ones already with peri-implantitis, which ones are currently loosing bone and which ones are going to progress.

This project aims to analyze for the first time the whole large scale proteome and metabolome of peri-implant crevicular fluid (PICF) with an integrated approach from implants with peri-implant diseases.

Twenty-five patients with at least one implant with peri-implant mucositis and one implant with peri-implantitis will be selected. For each of the selected participants, the PICF from an implant with peri-implant mucositis and from an implant with peri-implantitis will be sampled two different times before treatment. One year after the corresponding treatment is provided, the PICF of the treated implants with peri-implantitis will be sampled again.

The proteomic analyses will be carried out though an integrated top-down approach, while the metabolomic ones through MS-based methods (HPLC-API-MS/MS, FIA-APIMS/MS and GC-MS) with the aim of study the total untargeted (unbiased) proteome and metabolome. The most important strength of this project will be the ability to evaluate together the whole proteome and the whole metabolome and to integrate them in the same framework.

The ultimate long-term perspective of this project will be to improve the knowledge of the disease pathogenetic mechanisms, to cluster different types of peri-implant diseases according to their biochemical profile and to construct predictive/diagnostic tests in order to:
- predict peri-implantitis onset in implants with peri-implant mucositis;
- allow early diagnosis of peri-implantitis when peri-implantitis has occurred;
- identify disease activity (the implants who are currently loosing bone);
- allow the early identification of high-risk implants to provide stricter preventive procedures and early treatment;
- predict which implants are going to respond well to the conventional treatments and to use this information in the direction of personalized medicine, in order to study new therapeutic alternatives for the no-responders utilizing such biomarkers as new early outcomes of disease resolution.