Gingival crevicular fluid and its immune mediators in the proteomic era
Abstract
The discovery of biomarkers for periodontal disease requires an in‐depth understanding of the molecular basis of the initiation and progression of the disease. The gingival crevicular fluid is a biological medium suitable for identifying and measuring such biomarkers because it can be easily and noninvasively sampled from the immediate vicinity of the affected tissues. An ever‐expanding pool of gingival crevicular fluid proteins associated with periodontal health or disease has been catalogued over the years, particularly with the recent implementation of proteomic technologies. ‘Proteomics’ refers to the large‐scale study of entire arrays of proteins expressed by a genome and present in a cell, tissue, biological fluid or organism. Hence, such technologies provide a broad qualitative and quantitative insight of the proteins present in gingival crevicular fluid. Pertinent studies have amassed on the information gathered to date on protein signatures in periodontal health and disease, and have confirmed the nature of the immunological host response. This review discusses the application of proteomic technologies in characterizing the molecular networks present in gingival crevicular fluid, their potential for discovery of biomarkers that are meaningful for clinical practice, and the associated technical challenges.
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The absorption technique through paper strips is the easiest and most accurate method for obtaining gingival crevicular fluid exudate.
“GCF collection methods include intracrevicular lavage technique, microcapillary technique and absorption technique through the use of paper strips being the easiest and most accurate method for obtaining such exudate [90].”
From Potential Impact of Prosthetic Biomaterials on the Periodontium: A Comprehensive Review (2023) · cites this paper as [90]
Proteomic applications have been applied to understand changes in the proteome that diverge states of periodontal health from disease and to further characterize various periodontal disease states, including gingivitis, mild, moderate, chronic, and aggressive periodontitis.
“Proteomic applications have been applied to understand changes in the proteome that diverge states of periodontal health from disease and to further characterize various periodontal disease states, including gingivitis, mild, moderate, chronic, and aggressive periodontitis.70, 101, 102, 103, 104”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [102]
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- Authors
- Bostanci, N., & Belibasakis, G. N.
- Venue
- Periodontology 2000
- Publisher
- Wiley
- Published
- 2017-11-30
- Volume
- 76
- Issue
- 1
- Pages
- 68-84
- ISSN
- 0906-6713 (ISSN-L)
- 1600-0757
- OpenAlex
- W2773076305
- Cited by
- 109
- References
- 89
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 17 · 2023: 10 · 2022: 9 · 2021: 20 · 2020: 9 · 2019: 10 · 2018: 8 · 2017: 2
- FWCI
- 1.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Periodontology 2000 (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Health Sciences › Medicine › Urology › Periodontal Regeneration and Treatments
- Health Sciences › Health Professions › Pharmacy › Oral and gingival health research
- Keywords
- gingival crevicular fluid, periodontal disease, biomarkers, proteomics, host immune response, periodontitis, non-invasive diagnosis
- MeSH terms
- Diagnosis, Oral, Gingival Crevicular Fluid, Humans, Immunologic Factors, Inflammation, Periodontal Pocket, Biomarkers, Cytokines, Disease Progression, Proteomics, High-Throughput Screening Assays
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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