Cytokine-based Predictive Models to Estimate the Probability of Chronic Periodontitis: Development of Diagnostic Nomograms.
Sci Rep
; 7(1): 11580, 2017 09 14.
Article
in En
| MEDLINE
| ID: mdl-28912468
ABSTRACT
Although a distinct cytokine profile has been described in the gingival crevicular fluid (GCF) of patients with chronic periodontitis, there is no evidence of GCF cytokine-based predictive models being used to diagnose the disease. Our objectives were to obtain GCF cytokine-based predictive models; and develop nomograms derived from them. A sample of 150 participants was recruited 75 periodontally healthy controls and 75 subjects affected by chronic periodontitis. Sixteen mediators were measured in GCF using the Luminex 100™ instrument GMCSF, IFNgamma, IL1alpha, IL1beta, IL2, IL3, IL4, IL5, IL6, IL10, IL12p40, IL12p70, IL13, IL17A, IL17F and TNFalpha. Cytokine-based models were obtained using multivariate binary logistic regression. Models were selected for their ability to predict chronic periodontitis, considering the different role of the cytokines involved in the inflammatory process. The outstanding predictive accuracy of the resulting smoking-adjusted models showed that IL1alpha, IL1beta and IL17A in GCF are very good biomarkers for distinguishing patients with chronic periodontitis from periodontally healthy individuals. The predictive ability of these pro-inflammatory cytokines was increased by incorporating IFN gamma and IL10. The nomograms revealed the amount of periodontitis-associated imbalances between these cytokines with pro-inflammatory and anti-inflammatory effects in terms of a particular probability of having chronic periodontitis.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cytokines
/
Chronic Periodontitis
Type of study:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Sci Rep
Year:
2017
Document type:
Article
Affiliation country:
España