Gene Expression of Inflammation and Bone Healing in Peri‐Implant Crevicular Fluid after Placement and Loading of Dental Implants. A Kinetic Clinical Pilot Study Using Quantitative Real‐Time PCR

Abstract

Early detection of healing complications after placement of dental implants is a pressing but elusive goal. This paper proposes a non-invasive diagnostic tool for monitoring healing- and peri-implant disease specific genes, complementary to clinical evaluations. Eighteen partially edentulous patients were recruited to this pilot study. Three Brånemark TiUnite® implants/patient (Nobel Biocare) were placed in a one-stage procedure. Abutments with smooth or rough (TiUnite®) surface were placed. The test group (n = 9) received fixed bridges (immediate loading), whereas the control group (n = 9) implants were loaded 3 months after surgery. In addition to clinical measurements, crevicular fluid was collected using paper strips at the implant abutments 2, 14, 28, and 90 days postoperative. mRNA was extracted, purified, and converted to cDNA. Quantitative PCR assays for IL-1β, TNF-α, Osteocalcin (OC), Alkaline Phosphatase (ALP), Cathepsin K, Tartrate Resistant Acid Phosphatase, and 18S ribosomal RNA were designed and validated. Relative gene expression levels were calculated. One implant was lost in the control group and three in the test group. In one test patient, one implant showed lowered stability after 2 to 4 weeks and was unloaded. Later implant stability improved which allowed for loading after 3 to 4 months. TNF-α and ALP most commonly showed correlation with clinical parameters followed by IL-1β and OC. The strongest correlation was found for TNF-α with clinical complications at 2 and 14 days (p = .01/r = -048, and p = .0004/r = -0.56, respectively; test and control groups together). In some cases, gene expression predicted clinical complications (TNF-α, ALP, CK). This study is based on samples from few individuals; still, some genes showed correlation with clinical findings. Further studies are needed to refine and optimize the sampling process, to find the appropriate panel, and to validate gene expression for monitoring implant healing.