Association and Predictive Value of C-Reactive Protein, Fibrinogen and Periodontal Indices in Periodontitis Severity : A Retrospective study

Objectives : Periodontitis is a chronic inflammatory disease closely linked to systemic conditions. This study evaluated the clinical utility of systemic inflammatory markers and periodontal parameters in assessing periodontitis severity and explored their combined predictive value for identifying advanced disease. Materials and Methods: A total of 215 participants were enrolled in a cross-sectional study and categorized into Stage I (2.3%), Stage II (9.3%), Stage III (63.3%), and Stage IV (25.1%) periodontitis based on clinical assessments. Periodontal parameters, including plaque index (PLI), gingival index (GI), bleeding index (BI), probing depth (PD), and clinical attachment loss (AL), were recorded. Venous blood samples were collected to determine serum C-reactive protein (CRP) and plasma fibrinogen (Fg) levels. Correlation analyses and multivariate binary logistic regression were performed to assess the relationships and predictive value of these parameters. Results : Age differed significantly across disease stages (P < 0.05), while gender, hypertension, and diabetes did not (P > 0.05). PD, AL, CRP, and Fg increased progressively with disease severity (P < 0.05), whereas PLI, GI, and BI showed no significant trends. CRP correlated with PD, AL, and Fg (P < 0.05). The predictive model combining CRP, Fg, PD, and AL achieved an AUC of 0.885. Logistic regression identified CRP (OR: 1.97), PD (OR: 1.47), and AL (OR: 1.98) as independent predictors of Stage IV periodontitis (P < 0.05), while Fg lost significance after adjustment (P = 0.107). Conclusion: A composite model integrating CRP, Fg, PD, and AL effectively predicts periodontitis severity, supporting its use for early detection and personalized management. Clinical Relevance: CRP and Fg, widely recognized as inflammatory mediators in systemic disease, are also implicated in periodontal inflammation. By combining CRP, Fg, PD, and AL, we developed a predictive model that provides a comprehensive and clinically meaningful tool for identifying patients at high risk of severe periodontitis.