The potential of salivary biomarkers for predicting the sensitivity and monitoring the response to nonsurgical periodontal therapy: A preliminary assessment

Lee, C.‐H.; Chen, Yi-Wen; Tu, Yu–Kang; Wu, Yun-Chun; Chang, Ping-Chuan

doi:10.1111/jre.12544

Cited by 26 publications

(19 citation statements)

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“…Furthermore, higher NGAL levels in local samples, that is, gingival crevicular fluid (GCF), were reported in patients with periodontitis (Pradeep, Nagpal, Karvekar, & Patnaik, ). The present salivary NGAL data are therefore intriguing and warrant further study, but it is remarkable that conflicting data also exist for other salivary inflammatory markers in periodontitis, for example, with increased (Lee et al, ) or similar (Moura et al, ) salivary levels of IL‐1β compared to healthy controls. While the main contributor to salivary proteins is the circulation, with proteins shed from local oral surfaces playing a lesser role (Lynge Pedersen & Belstrom, ), disease‐specific mechanisms are likely to be involved, for example, with periodontitis‐dependent increased expression of inflammatory mediators in the inflamed oral tissue potentially being countered by increased local tissue binding or other interactions that contribute to unpredictability of salivary levels of these molecules.…”

Section: Discussionmentioning

confidence: 72%

“…Saliva is the fluid of the oral cavity, which besides being critical to maintenance of oral homeostasis harbors various biological substances such as the salivary microbiota and inflammatory markers (Lynge Pedersen & Belstrom, ). The presence of untreated periodontitis has been shown to alter the composition of the salivary microbiota (Belstrom et al, ) and increase salivary levels of inflammatory proteins such interleukin (IL)‐1β and matrix metalloproteinase (MMP)‐8 (Lee, Chen, Tu, Wu, & Chang, ; Liukkonen, Gürsoy, Pussinen, Suominen, & Könönen, ; Sorsa et al, ). Thus, the composition of the salivary microbiota and salivary levels of inflammatory markers have been suggested as a proxy of oral and systemic health status (Yoshizawa et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Salivary microbiota and inflammation‐related proteins in patients with psoriasis

et al. 2020

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Objective The purpose of the present study was to characterize the composition of the salivary microbiota and quantify salivary levels of inflammation‐related proteins (neutrophil gelatinase‐associated lipocalin [NGAL] and transferrin) in patients with psoriasis and compare data to those obtained in patients with periodontitis and orally healthy controls, respectively. Materials and methods Stimulated saliva samples from patients with psoriasis (n = 27), patients with periodontitis (n = 58), and orally healthy controls (n = 52) were characterized by means of next‐generation sequencing of the 16S rRNA gene. Salivary levels of NGAL and transferrin were quantified using immunoassays. Results Linear discriminant effect size analysis showed that 52 (22 psoriasis‐associated and 30 periodontitis‐associated) and 21 (8 psoriasis‐associated and 13 orally healthy control‐associated) bacterial taxa differentiated the salivary microbiota in patients with psoriasis from that of patients with periodontitis and orally healthy controls, respectively. Significantly lower mean salivary levels of NGAL (psoriasis: 996 [std. error 320], periodontitis: 2,072 [295], orally healthy controls: 2,551 [345] ng/ml, p < .0001) and transferrin (psoriasis: 4.37 [0.92], periodontitis: 7.25 [0.88], orally healthy controls: 10.02 [0.94] ng/ml, p < .0001) were identified in patients with psoriasis. Conclusions Psoriasis associates with characteristics of the salivary microbiota and salivary levels of inflammation‐related proteins, which are different from characteristics in patients with periodontitis and orally healthy controls, respectively.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Salivary microbiota and inflammation‐related proteins in patients with psoriasis

et al. 2020

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“…The potential of salivary IL-1β and MMP-8 to predict the response to treatment has been reported in a few studies. Lee et al (2018) found that the baseline levels of IL-1β and MMP-8 in saliva…”

Section: F I G U R Ementioning

confidence: 99%

“…Using saliva as a diagnostic fluid for point-of-care analysis for periodontal diseases is advantageous because sample collection is easy and non-invasive, and there is access to plentiful biological fluid (Korte & Kinney, 2016). Some biomarkers in saliva have been shown to be related to the treatment response to SRP (Lee et al, 2018;Nagarajan et al, 2019;Sexton et al, 2011) and could potentially be used to estimate the treatment response to SRP.…”

Section: Introductionmentioning

confidence: 99%

The combined use of salivary biomarkers and clinical parameters to predict the outcome of scaling and root planing: A cohort study

Liu

Duan

et al. 2020

J Clinic Periodontology

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Aim: To explore the application of the combined use of baseline salivary biomarkers and clinical parameters in predicting the outcome of scaling and root planing (SRP). Materials and methods: Forty patients with advanced periodontitis were included. Baseline saliva samples were analysed for interleukin-1β (IL-1β), matrix metalloproteinase-8 and the loads of Porphyromonas gingivalis, Prevotella intermedia, Aggregatibacter actinomycetemcomitans and Tannerella forsythia. After SRP, pocket closure and further attachment loss at 6 months post-treatment were chosen as outcome variables. Models to predict the outcomes were established by generalized estimating equations. Results: The combined use of baseline clinical attachment level (CAL), site location and IL-1β (area under the curve [AUC] = 0.764) better predicted pocket closure than probing depth (AUC = 0.672), CAL (AUC = 0.679), site location (AUC = 0.654) or IL-1β (AUC = 0.579) alone. The combination of site location, tooth loss, percentage of deep pockets, detection of A. actinomycetemcomitans and T. forsythia load (AUC = 0.842) better predicted further clinical attachment loss than site location (AUC = 0.715), tooth loss (AUC = 0.530), percentage of deep pockets (AUC = 0.659) or T. forsythia load (AUC = 0.647) alone. Conclusion: The combination of baseline salivary biomarkers and clinical parameters better predicted SRP outcomes than each alone. The current study indicates the possible usefulness of salivary biomarkers in addition to tooth-related parameters in predicting SRP outcomes.

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“…Although it has been suggested that IL1β can discriminate between inactive and active periodontal lesions, there are very few diagnostic accuracy studies that have investigated it in saliva as a way to evaluate the response to periodontal treatment (Lee, Chen, Tu, Wu, & Chang, 2018; Shyu et al, 2015). The methodological approach in the present paper is different because we evaluate for the first time the capacity of IL1β in saliva to distinguish untreated from treated periodontitis demonstrating clinical improvement and differentiating between non‐smokers and smokers.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of IL1β in saliva: The development of predictive models for estimating the probability of the occurrence of periodontitis in non‐smokers and smokers

Arias-Bujanda

Regueira-Iglesias

Blanco-Pintos

et al. 2020

J Clinic Periodontology

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Aim:To obtain salivary interleukin (IL) 1β-based models to predict the probability of the occurrence of periodontitis, differentiating by smoking habit. Materials/Methods:A total of 141 participants were recruited, 62 periodontally healthy controls and 79 subjects affected by periodontitis. Fifty of the diseased patients were given non-surgical periodontal treatment and showed significant clinical improvement in 2 months. IL1β was measured in the salivary samples using the Luminex instrument. Binary logistic regression models were obtained to differentiate untreated periodontitis from periodontal health (first modelling) and untreated periodontitis from treated periodontitis (second modelling), distinguishing between nonsmokers and smokers. The area under the curve (AUC) and classification measures were calculated. Results:In the first modelling, IL1β presented AUC values of 0.830 for non-smokers and 0.689 for smokers (accuracy = 77.6% and 70.7%, respectively). In the second, the predictive models revealed AUC values of 0.671 for non-smokers and 0.708 for smokers (accuracy = 70.0% and 75.0%, respectively). Conclusion:Salivary IL1β has an excellent diagnostic capability when it comes to distinguishing systemically healthy patients with untreated periodontitis from those who are periodontally healthy, although this discriminatory potential is reduced in smokers. The diagnostic capacity of salivary IL1β remains acceptable for differentiating between untreated and treated periodontitis. K E Y W O R D S diagnostic accuracy, interleukin 1β, periodontitis, predictive values, prevalence, saliva, sensitivity, specificity | 703 ARIAS-BUJANDA et Al.

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The potential of salivary biomarkers for predicting the sensitivity and monitoring the response to nonsurgical periodontal therapy: A preliminary assessment

Abstract: Salivary IL-1β and MMP-8 might be useful for diagnosing periodontitis and monitoring the recovery of periodontitis following nonsurgical periodontal therapy. MMP-8 and lactoferrin showed potential for predicting the sensitivity to the treatment.

Cited by 26 publications

References 58 publications

Salivary microbiota and inflammation‐related proteins in patients with psoriasis

Salivary microbiota and inflammation‐related proteins in patients with psoriasis

The combined use of salivary biomarkers and clinical parameters to predict the outcome of scaling and root planing: A cohort study

Diagnostic accuracy of IL1β in saliva: The development of predictive models for estimating the probability of the occurrence of periodontitis in non‐smokers and smokers

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