Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

Abstract

Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss <4 mm and >4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa), Fusobacterium nucleatum (Fn), Parvimonas micra (Pm), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Tannerella forsythia (Tf), and Treponema denticola (Td). The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ≥0.760 and a sensitivity and specificity ≥75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky’s complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa, while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitis.

Microbiome and Culture Based Analysis of Chronic Rhinosinusitis Compared to Healthy Sinus Mucosa

“…The genera Flavobacteria, Pedobacter, Brevundimonas , and Achromobacter do not comprise relevant human pathogens. Members of the genus Porphyromonas can cause periodontitis and root canal abscesses (Nickles et al, 2016 ; Nobrega et al, 2016 ; Tomas et al, 2017 ) and were associated with CRS in CoNet analyses in a recent meta-analysis of the 16S rRNA sequencing data (Wagner Mackenzie et al, 2017 ). Members of the genera Stenotrophomonas and Pseudomonas are frequently involved in hospital acquired infections and have been already associated with CRS in many culture-based studies (Brook, 2016 ).…”

“…Only in two cases of unilateral maxillary CRS the genera Fusobacterium, Prevotella , and Parvimonas were found at high abundance in the cultivation independent analysis, while in parallel F. nucleatum, Parvimonas micra , and Prevotalla oris , were found in the cultivation based study in high quantities. These bacteria are typical members of the oral flora and regularly cultivated from chronic periodontitis (Nickles et al, 2016 ; Tomas et al, 2017 ). They are known as pathogens in acute root canal infections (Nobrega et al, 2016 ) and particularly Fusobacteria have a high virulence and are frequently involved in severe infections such as peritonsillar abscesses (Powell et al, 2013 ) and intracranial sinugenic complications (Gallagher et al, 1998 ).…”

Estimation of neopterin as a biomarker in biofluids of pre and post-menopausal women after initial periodontal therapy: A biochemical assay

“…The main problem in periodontal diagnosis is determining the early onset of PDD by biomarkers. [ 9 ]…”

Development of an miRNA-Array-Based Diagnostic Signature for Periodontitis

“…For the combination diagnostic models, the combination of IL-1β, IL-1ra, and MMP exhibited the highest AUC (0.853) with high sensitivity (73.3%) and specificity (88.9%) (Wu et al, 2018). The TdPiTfAaFnPm bacterial cluster-based model has an AUC ≥ 0.787 with a sensitivity and specificity ≥ 80.0% (Tomas et al, 2017). However, there are limited reports on non-coding RNAs as biomarkers for periodontitis.…”