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

U

Universidad Complutense de Madrid

Status

Not yet enrolling

Conditions

Periodontal Diseases

Treatments

Other: No intervention

Study type

Observational

Funder types

Other

Identifiers

NCT07167771
165-290725

Details and patient eligibility

About

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.

Enrollment

100 estimated patients

Sex

All

Ages

18+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  • Participants from the cohort involved in the previous cross-sectional study (code 23/481-E).
  • Adults (≥ 18-year-old)
  • Being able to sign an informed consent form
  • Willing to participate in this observational investigation
  • Diagnosed as periodontally healthy, gingivitis, treated periodontitis patient (stable / unstable), periodontitis stages I & II, or periodontitis stages III and IV (Papapanou et al. 2023) in the previous cross-sectional study (code 23/481-E)

Exclusion criteria

  • Patients fitting to all the above inclusion criteria will be excluded from the study if unable to attend to the study-related procedures.

Trial design

100 participants in 1 patient group

Cohort 1
Description:
Participants involved in an already done cross-sectional study (code 23/481-E).
Treatment:
Other: No intervention

Trial contacts and locations

1

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Central trial contact

Mariano Sanz, Prof.; Elena Figuero, Prof.

Data sourced from clinicaltrials.gov

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