Cited by 29Oral microbiology and periodontitis researchAdvanced Proteomics Techniques and ApplicationsPeriodontal Regeneration and Treatments

High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis

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Guzman, Y. A., Sakellari, D., Papadimitriou, K., Floudas, C. A.·2018·Journal of Periodontal Research

Abstract

Background and Objective: Untargeted, high‐throughput proteomics methodologies have great potential to aid in identifying biomarkers for the diagnosis of periodontal disease. The application of such methods to the discovery of candidate biomarkers for the resolution of periodontal inflammation after periodontal therapy has been investigated. Material and Methods: Gingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis at baseline and 1, 5, 9 and 13 weeks after completion of mechanical periodontal treatment. Clinical indices of periodontal disease, including probing depth, recession, clinical attachment level and bleeding on probing, were recorded at baseline and 13 weeks. Samples were analyzed using an online liquid chromatography‐nanoelectrospray‐hybrid ion trap‐Orbitrap mass spectrometer. Spectra were processed with the PILOT _ PROTEIN proteomics software suite. Results: Clinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test, P < .05). From the substantial number of identified proteins, a small subset was extracted by filter methods that included temporal pattern matching, logistic function fitting and mixed‐integer linear optimization. This subset includes azurocidin, lysozyme C and myosin‐9 as candidate biomarkers prominent at baseline and alpha‐smooth muscle actin as prominent 13 weeks after treatment. Cross‐validation studies yielded average predictive accuracy and area under the curve of 0.900 and 0.930, respectively. Conclusion: High‐throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.

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  1. [27]Guzman YA Theoretical advances in robust optimization, feature selection,and biomarker discovery 2016 Doctoral dissertation Princeton University

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Authors
Guzman, Y. A., Sakellari, D., Papadimitriou, K., & Floudas, C. A.
Affiliations
  • Princeton University (US)
  • Aristotle University of Thessaloniki (GR)
  • Texas A&M University (US)
Corresponding author
Sakellari, D.
Venue
Journal of Periodontal Research
Publisher
Wiley
Published
2018-06-14
Volume
53
Issue
5
Pages
853-860
ISSN
  • 0022-3484 (ISSN-L)
  • 1600-0765
OpenAlex
W2807871813
Cited by
29
References
48
Citations by year
2026: 2 · 2025: 4 · 2024: 7 · 2023: 2 · 2022: 1 · 2021: 4 · 2020: 5 · 2019: 3
FWCI
3.45
Type
journal article
Language
en
Open access
Locations
  • Journal of Periodontal Research (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (r01lm009338)
  • National Science Foundation (CBET‐0941143)
Topics
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Health Sciences › Medicine › Urology › Periodontal Regeneration and Treatments
Keywords
chronic periodontitis, gingival crevicular fluid, biomarkers, periodontal treatment, heparin-binding protein, lysozyme C, myosin-9, alpha-smooth muscle actin, proteomic biomarker discovery, treatment endpoints
MeSH terms
Chromatography, Liquid, Female, Gingival Crevicular Fluid, Humans, Male, Middle Aged, Periodontal Index, Biomarkers, Spectrometry, Mass, Electrospray Ionization, Nanotechnology, Proteomics, Chronic Periodontitis

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