ArticleJournal of clinical periodontology2013
Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization.
Article in Journal of clinical periodontology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- MS-Based Crevicular Fluid Proteomics for the Study of Periodontal and Peri-Implant Conditions: A Systematic Review.Journal of proteome research · 2026Pooled it
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Gingival Crevicular Fluid Proteomic Shift: From Gingivitis to Periodontitis.International dental journal · 2026Article
- Review
- A multi-platform analysis of human gingival crevicular fluid reveals ferroptosis as a relevant regulated cell death mechanism during the clinical progression of periodontitis.International journal of oral science · 2024Article
- The Expression Patterns of Non-Coding RNAs in Periodontal Disease.Dentistry journal · 2024Review
- MALDI-TOF/MS Profiling of Whole Saliva and Gingival Crevicular Fluid in Patients with the Invisalign System and Fixed Orthodontic Appliances.International journal of environmental research and public health · 2023Article
- Proteomics Disclose the Potential of Gingival Crevicular Fluid (GCF) as a Source of Biomarkers for Severe Periodontitis.Materials (Basel, Switzerland) · 2022Article
- Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases.International journal of molecular sciences · 2020Review
- Review
- High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis.Journal of periodontal research · 2018Article
- Gingival Crevicular Fluid as a Novel Potential Source of Biomarkers Distinguishes Pubertal from Post-Pubertal Subjects.Diagnostics (Basel, Switzerland) · 2016Article
- Gingival crevicular fluid as a source of biomarkers for periodontitis.Periodontology 2000 · 2016Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
aimTo identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets. MATERIALS AND
methodsGCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased.
resultsA thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples.
conclusionsThe proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.