Search

ABSTRACT Aim Material and Methods Results Conclusions To discover new salivary biomarkers to diagnose periodontitis and evaluate the impact of age and smoking on predictive capacity.Saliva samples were collected from 44 healthy periodontal individuals and 41 with periodontitis. Samples were analysed by sequential window acquisition of all theoretical mass spectra (SWATH‐MS), and proteins were identified by employing the UniProt database. The diagnostic capacity of the molecules was determined with generalized additive models. The models obtained were single‐protein unadjusted and adjusted for age and smoking status, besides two‐protein combinations.Eight single salivary proteins had a bias‐corrected accuracy (bc‐ACC) of 78.8%–86.8% (bc‐sensitivity/bc‐specificity of 62.5%–86.9%/60.9%–98.1%) to diagnose periodontitis. Predictive capacity increased more by adjusting for age (bc‐ACC: 94.1%–98.2%; bc‐sensitivity/bc‐specificity: 90.2%–98.6%/93.6%–97.2%) than smoking (bc‐ACC: 83.9%–90.4%; bc‐sensitivity/bc‐specificity: 73.6%–89.9%/76.2%–96.4%). These proteins were keratin, type II cytoskeletal 1, protein S100‐A8, β‐2‐microglobulin, neutrophil defensin 1, lysozyme C, ubiquitin‐60S ribosomal protein L40, isoform 2 of tropomyosin α‐3 chain and resistin. Two dual combinations showed bc‐sensitivity/bc‐specificity of > 90%: β‐2‐microglobulin with profilin‐1, and lysozyme C with zymogen granule protein 16 homologue B.New salivary biomarkers show good or excellent ability to diagnose periodontitis. Age has a more significant influence on the accuracy of the single biomarkers than smoking, with results comparable to two‐protein combinations. [ABSTRACT FROM AUTHOR]

Aim: To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH‐MS). Materials and Methods: GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III–IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH‐MS, and proteins were identified by the UniProt human‐specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions. Results: In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias‐corrected (bc)‐sensitivity/bc‐specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP‐9, MMP‐8, zinc‐α‐2‐glycoprotein and neutrophil gelatinase‐associated lipocalin and ZG16B with cornulin provided increased bc‐sensitivity/bc‐specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions. Conclusions: New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings. [ABSTRACT FROM AUTHOR]

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 non-smokers 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., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)