CROSS SECTIONAL CLINICAL STUDY TO DETERMINE THE PREDICTIVE ABILITY OF A PANEL OF BIOMARKERS IN SALIVA IN HEALTHY AND PERIODONTALLY AFFECTED SUBJECTS. PILOT STUDY

Objective: The main aim of this cross-sectional clinical study is to evaluate the predictive ability of a panel of salivary biomarkers in determining periodontal health status.

Material and methods: In this observational, cross-sectional study patients attending consecutively to the Periodontal Postgraduate Clinic at the University Complutense of Madrid. The participants will be categorized into different periodontal health status groups based on the 2018 classification of periodontal diseases, including periodontally healthy, gingivitis, treated periodontitis (stable/unstable), and various stages of periodontitis. During the screening visit, participants will undergo a comprehensive medical examination to gather relevant health information, including age, gender, weight, height, waist circumference, and drug, alcohol, and smoking history. Additionally, clinical assessments, saliva samples and microbiological parameters will be recorded. A convenience sample of 100 subjects will be recruited for this pilot study with the objective to generate data for the multivariate predictive analysis. 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