Diagnostic Accuracy of Self-Reported Parameters, Oral and Systemic Biomarker Profile for the Detection of Periodontal Health and Disease
Diagnostic Accuracy of Self-Reported Parameters, Oral and Systemic Biomarker Profile for the Detection of Periodontal Health and Disease
The majority of the burden of periodontal diseases in the population remain undetected. Self-detection and confirmation with simple, non-clinical tests may improve early case detection and access to the needed level of care. A recently conducted study has indicated that self-reported signs and symptoms through questionnaires and gingival bleeding on brushing (GBoB), in particular, are potentially useful approaches to detect gingival inflammation and other signs of periodontal health and disease. A parallel study evaluated the accuracy of the presence of elevated levels of activated matrix metalloproteinase-8 (MMP-8). A strategy combining specific questions, subject demographics, GBoB and aMMP-8 has shown promise for screening and diagnosis of periodontal health and disease. Validation of a diagnostic approach requires assessment in a first population (development group) and confirmation in an independent one.
Periodontal disease (mostly plaque-induced gingivitis and periodontitis) is probably the most prevalent non-communicable diseases of mankind, with substantial socio-economic impacts and considerable effects on individuals' oral health and general health/well-being (Jin et al., 2016; Listl et al., 2015; Tonetti et al., 2017). Currently, periodontal disease in the population remains largely undetected. It is therefore of great importance to enhance the awareness of periodontal health and improve the early diagnosis of periodontal disease for effective care.
Although the clinical examinations are considered the gold standard for the diagnosis, there are several sets of limitations, such as its laborious and time-consuming process that requires highly skilled dental practitioners, the lack of adequate precision for the detection of incipient periodontitis, and the insufficiency of reflecting ongoing disease activity/risk of progression events. Consequently, alternative cost-effective but reliable and valid approaches for periodontal screening/diagnosis particularly in public communities are highly needed.
Self-detection and confirmation with simple, non-clinical tests may improve early case detection and access to the needed level of care. The recent findings from our group have indicated that self-reported signs and symptoms through questionnaires and toothbrushing testing for Gingival Bleeding on brushing (GBoB), are potentially useful approaches to detect gingival inflammation and other signs of periodontal health and disease (Deng et al., 2021a; Tonetti et al., 2020). Moreover, oral biomarkers can give an indication of the probable disease status and allow monitoring of the biochemical processes associated with periodontal disease. A parallel study that evaluated the diagnostic utility of a point-of-care test for the activated matrix metalloproteinase-8 (aMMP-8), a biomarker associated with the collagen degradation of periodontium in periodontitis, has shown a significant association but limited accuracy for periodontitis (Deng et al., 2021b). In addition, increasing evidence suggests that the local inflammatory and/or infectious burden might trigger a systemic host response and alter the individual metabolic status. It is therefore logical to employ metabolic and inflammatory markers for estimating the risk of systemic inflammatory burden of periodontitis and to assess their relationship with the grading and staging of periodontitis based on the 2017 classification.
Notably, findings from our recent study revealed that a strategy combining specific questions, subject demographics, GBoB and aMMP-8 has good performance for differentiating periodontal health, gingivitis and periodontitis (unpublished). Despite a promising potential of the screening/diagnostic models developed from our initial investigation, it is essential to externally validate them in an independent population because a prediction rule derived from one sample does not necessarily perform well in a different sample/population.
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maurizio.tonetti@ergoperio.eu15000102368