Genomics in dental implantology: The role of genetic and epigenetic factors in dental implant failure – a narrative review

Dental implants are a widely used solution for tooth replacement, yet implant failures remain a challenge. Genetic predispositions and epigenetic modifications influence osseointegration and peri-implant health. The present review explores genetic mechanisms affecting implant healing and introduces implantogenomics - a personalized approach to implant therapy based on an individual's genetic profile.A comprehensive review of literature from PubMed®, Scopus, EMBASE, and Web of Science (2008-2024) was conducted using Medical Subject Headings (MeSH) terms such as "genetic markers," "implantogenomics" and "epigenetics." After removing duplicates and screening for relevance, a total of 46 studies were included in the analysis.Key genetic variants in bone metabolism (collagen type 1 alpha 1 (COL1A1), runt-related transcription factor 2 (RUNX2), vitamin D receptor (VDR)), immune response (interleukin-1 (IL-1), tumor necrosis factor alpha (TNF-α), IL-6), and osseointegration-related genes (osteoprotegerin (OPG), receptor activator of nuclear factor kappa B (RANK), receptor activator of nuclear factor kappa-B ligand (RANKL)) were identified as potential contributors to implant failure. Epigenetic modifications, including DNA methylation, histone changes and microRNAs (miRNAs), regulate bone remodeling and immune responses, and have an influence on implant integration. Advances in genomics have paved the way for personalized implant therapy through genetic screening, optimizing outcomes and reducing the number of implant failures. Implantogenomics is aimed at tailoring treatments based on genetic profiles, while epigenetic therapies, such as gene modulation, enhance implant integration. Future research should focus on predictive biomarkers and precision-based strategies to improve implant longevity. Genetic and epigenetic factors play a crucial role in the success of dental implants. Integrating genomic insights into clinical practice can enhance patient selection, predict implant success and improve treatment outcomes. Further research is necessary to establish predictive biomarkers and targeted interventions.