LONGITUDINAL FOLLOW-UP STUDY TO DETERMINE THE PREDICTIVE ABILITY OF A PANEL OF BIOMARKERS IN SALIVA IN HEALTHY AND PERIODONTALLY AFFECTED PATIENTS

Objective: The main aim of this longitudinal clinical study is to evaluate the predictive ability of a panel of salivary biomarkers in determining periodontal health status in 2 years follow up in a group of healthy and periodontally affected individuals. Material and methods: In this longitudinal, observational follow-up study, patients previously enrolled in a cross-sectional study at the Periodontal Postgraduate Clinic, University Complutense of Madrid, will be re-evaluated over a 2-year period. Participants (≥18 years) will be categorized into diagnostic groups based on the 2018 classification of periodontal diseases, including periodontally healthy, gingivitis, treated periodontitis (stable/unstable), and various stages of periodontitis. The study will include follow-up visits at 1 and 2 years. At each visit, participants will undergo a comprehensive medical examination to assess age, gender, weight, height, waist circumference, blood pressure, temperature, smoking and alcohol history, systemic health, and HbA1c levels. A periodontal examination will be performed at six sites per tooth, and clinical parameters including plaque, bleeding on probing, probing depth, recession, and tooth loss will be recorded. Saliva and subgingival plaque samples will be collected for biomarker and microbiological analysis. Salivary biomarkers will be measured using multiplex immunoassays, and bacterial quantification will be performed by multiplex qPCR. Data analyses: Descriptive statistics will be used to report the clinical variables and patients will be grouped according to the pre-established diagnostic categories (periodontally healthy, gingivitis, treated periodontitis patient. In order to determine the possible statistical relationship with the medical, biochemical and microbiological variables assessed, a crude bivariate analysis will first be performed by applying a mean comparison test for quantitative variables (ANOVA) and a proportion comparison test for categorical variables (Chi-square). Subsequently, those variables identified as relevant in the crude analyses will be included as confounding and/or interaction factors in a binary logistic regression model, considering the presence of periodontitis as a response variable, in order to obtain crude and adjusted OR values, together with their corresponding 95% CIs. Based on the results obtained in the biomarker analysis, a relevant statistical analysis will be performed, taking into account all the variables collected in the study. For periodontitis cases, treatment response over time will be analyzed, with subgroup comparisons between responders and non-responders.