SYSTEMATIC REVIEW OF LABORATORY BIOMARKERS IN ORAL AND DENTAL DISEASES: DIAGNOSTIC AND PROGNOSTIC SIGNIFICANCE EVIDENCE

Abstract

Background: Laboratory biomarkers derived from saliva have been recognized as future tools for non-invasive diagnosis and prognosis of oral and dental diseases. They also possess the promise for systemic disease detection and education, but a review of their scientific and clinical application is currently lacking. Objective: The diagnostic and prognostic significance of laboratory biomarkers in oral and dental diseases like dental caries, periodontitis, and OSCC and their functions in medical lab services and science education is determined in this systematic review. Methods: Following PRISMA guidelines, we conducted searches of PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar for English-language systematic reviews, case-control studies, and observational studies from 2015 to 2025. We considered studies that quantified salivary or systemic biomarkers for oral/dental disease or associated outcomes in healthcare or education. We extracted data on study characteristics, biomarkers, outcomes, and quality (with the NIH Quality Assessment Tool). Narrative synthesis was conducted due to methodological heterogeneity. Results: Fourteen studies were included, with eight being "Good" and six being "Fair" in quality. Salivary biomarkers (e.g., IL-6, IL-8, TNF-α, NLR, aMMP-8, miRNA) correlated consistently with dental caries, periodontitis, OSCC, and systemic diseases, and were of high diagnostic accuracy and prognostic value. Medical laboratories played a crucial role in disease surveillance, with AI integration enhancing efficiency. Laboratory-based education improved science involvement in learning. Challenges were non-standardization, low follow-up, and variability in saliva composition. Conclusions: Salivary biomarkers represent a low-cost, non-invasive means for oral and dental disease diagnosis and prognosis with potential applicability to medicine and education. Standardization, technological integration (e.g., AI, nanotechnology), and further validation are required to realize their clinical potential.