Peri-implant crevicular fluid biomarkers as discriminants of peri-implant health and disease

Abstract

Aim: The objective of this cross-sectional study was to examine the potential of peri-implant crevicular fluid (PICF) analytes to discriminate between peri-implant health and disease using a multi-biomarker approach.

Methods: We collected PICF samples from the mesio-buccal site of every implant (n = 145) from 52 subjects with peri-implantitis and measured the levels of 20 biomarkers using Luminex. We grouped implants and subjects based on the clinical characteristic of the sampled sites and implants into: healthy sites from healthy implants (HH), diseased sites from diseased implants (DD) and healthy sites from diseased implants (HD). The significance of the differences between the HH and DD groups was determined using general linear models controlling for false discovery rate. We used logistic regression to determine the best multi-biomarker models that could distinguish HH from DD subjects and HH from HD subjects.

Results: There were statistically significant differences between HH and DD groups for 12/20 biomarkers. Logistic regression resulted in a 6-biomarker model (Flt-3L, GM-CSF, IL-10, sCD40L, IL-17 and TNFα) that discriminated HH from DD subjects (AUC = 0.93) and a 3-biomarker model (IL-17, IL-1ra and vascular endothelial growth factor) that distinguished HH from DD subjects (AUC = 0.90).

Conclusion: PICF biomarkers might help discriminate peri-implant health from disease.

Keywords: biomarkers; cytokines; dental implant; peri-implantitis.