Periodontal disease has been diagnosed by clinical performance of BOP, PD, CAL and radiographic evidence of alveolar bone loss. Although their utility is reliable, but still costly and need clinician's professional experience. Saliva have been proved to be a high potential tool in early diagnosis and monitoring oral and systemic diseases[14, 15], and in the previous studies, a number of salivary markers have been demonstrated to be different between diseased and healthy controls, but up to now there is no clear and convinced biomarker that can be used for diagnosing periodontal disease.
We need a balance between microbial and host response to keep periodontal health, during the progress of periodontal disease, the balance were broken and bacterial invasion, host inflammatory response, tissue and bone destructions non-simultaneously occurred. After bacteria (Pg) invasion, markers of inflammation (IL-1β) are released, enzymes such as MMP-8 are produced and activated by host cells leading to the degradation of connective tissue, and bone degradation resulted in releasing ICTP into periodontal tissues and saliva. In the present study we selected these four markers (IL-1β, MMP-8, ICTP, Pg), evaluated their diagnostic efficiency of gingivitis and periodontitis and built a valuable prediction panel using their combinations.
To our knowledge, IL-1β is a well-known inflammatory stimulator that is discriminative between healthy and periodontal lesions; Pg is significantly associated with periodontal disease and has been used as a potential screening biomarker of periodontitis. In this study, both of IL-1β and Pg showed significant difference among three groups, their salivary level were increasing in the gingivitis group and more higher in the periodontitis group. Our data were in consistence with other studies[12, 19], and besides significant elevation in gingivitis and periodontitis groups, our data also demonstrated positively association of IL-1β and Pg with clinical indexes (PD and BOP), showed they indeed reflected the periodontal status and can be valuable candidates to predict periodontal disease.
Recent investigations showed MMP-8 to be an indicator especially for early periodontitis[20, 21]. In the current study, MMP-8 in the gingivitis and periodontitis groups showed significant higher compared to the healthy group, but there was no significant difference between periodontitis and gingivitis group. As we know, MMP-8 is one of the most prevalence host proteinases and is correlated with periodontal inflammatory destruction. Since our participants’ gingiva already expressed inflammation in the gingivitis and periodontitis groups, they presented higher level of MMP-8. The main difference between gingivitis and periodontitis groups in the present study was bone loss, there seems to be no relationship between MMP-8 and bone loss and resulted in no continue increasing of MMP-8 in the periodontitis group compared to the gingivitis group. The present results were in accordance with Morelli’s study, salivary MMP-8 exhibited significant difference between gingivitis and healthy groups, but no significant difference between periodontitis and gingivitis groups. Nascimento’s study also showed MMP-8 significantly increased during the process of experimental gingivitis, all these data indicated that MMP-8 increased with the development of gingival inflammation and kept stable in the subsequent stage of disease.
Before an effective diagnosis of periodontitis, considerable amount of alveolar bone destruction would have established, when we measured the damage of bone loss clinically, a 2 to 3 mm threshold changes are needed before it exhibiting obvious destruction, that may delay the diagnosis and treatment. As a breakdown product of Type I collagen, ICTP is the major constituent of alveolar bone and was considered to reflect alveolar bone degradation and periodontal disease activity. Not surprising in our study, ICTP was found to be no significant difference between gingivitis and healthy groups as there was no bone loss in these two groups. Apparent alveolar bone loss in the periodontitis group resulted in their ICTP level were significant higher compared to gingivitis and healthy groups, which is in accordance with Mishra and Giannobile’s studies and they concluded that increased ICTP can be used to differentiate active gingivitis from periodontitis, and Payne's results also stated that salivary ICTP concentrations is significantly associated with alveolar bone height loss.
After confirming their difference among periodontitis, gingivitis and healthy groups, we examined their ability to discriminate different periodontal clinical phenotypes. As a single marker, IL-1β showed the best diagnostic value of these four candidates: exhibited an AUC value of 0.88 with 94% sensitivity and 71% specificity to discriminate periodontitis from healthy subjects, an AUC value of 0.81 with 82% sensitivity and 76% specificity to discriminate gingivitis from healthy subjects and an AUC value of 0.67 with 69% sensitivity and 64% specificity to discriminate periodontitis from gingivitis subjects, this is valuable results and is consistent with data from Jaedicke’s and Nazar’s investigations, they concluded that IL-1β is the most robust salivary biomarkers for periodontal disease. Hassan’s results exhibited there was a positive relationship between salivary IL-1β and the gingival inflammation during pregnancy. These results supported us including IL-1β as a predictive overall indicator of gingivitis and periodontitis.
Different markers may be peaked at different course of disease and when biomarkers of host and microbe origin are combined, the detection of periodontitis maybe improved rather than when used individually[13, 30, 31], previous studies revealed stronger discriminatory power when IL-1β, MMP-8 and other markers were combined compared with single analysis, Pg and MMP-8 in combination, ICTP and MMP-8 in combination also exhibited more predictive values. Our data showed that IL-1β individually revealed an AUC value of 0.88 to discriminate periodontitis from healthy subjects, the combination of IL-1β, MMP-8 and Pg strong performance improvement to an AUC value of 0.92, that is consistent with Gursoy's results[35, 36], they calculated IL-1β, MMP-8 and Pg together to obtain a cumulative risk score that is highly related with advanced periodontitis. Although showed that biomarker combinations facilitated more robust prediction of periodontal progression and stability, our results were different from Gursoy’s results and demonstrated that IL-1β and ICTP in combination yielded a similar AUC value (0.920) to differentiate periodontitis from healthy subjects compared with the combination of IL-1β, MMP-8 and Pg (0.921), indicating that IL-1β and ICTP were more valuable for predicting periodontitis and the combination of IL-1β, ICTP and Pg exhibited the best AUC value (0.95) to discriminate periodontitis from healthy subjects .
In addition, as a nondestructive and reversible gingival inflammation stage, we enrolled gingivitis subjects into our study and assigned participants with more homogeneous clinical phenotypes. Our results showed that MMP-8 in the periodontitis group was not significantly elevated compared to gingivitis group, and ICTP in the gingivitis group was not significantly elevated compared to healthy group, indicating that for predicting the different status, different marker combinations should be used to achieve an effective diagnosing power. After logistic regression analysis, the combination of IL-1, ICTP and Pg not only yielded the best AUC value to discriminate periodontitis from healthy subjects, but also exhibited the best performance to discriminate the periodontitis from gingivitis subjects (AUC = 0.78). To discriminate gingivitis from healthy subjects, although IL-1β, MMP-8 and Pg together revealed the best AUC value of 0.86, IL-1β and MMP-8 in combination yielded a slight lower AUC value of 0.85, these AUC values were lower to the combination of discriminating the periodontitis from healthy subjects (IL-1, ICTP and Pg, AUC = 0.94), but still were all above acceptable AUC value of 0.75 and could be potentially used for the clinical diagnosis.