Materials and Methods: One‐hundred and ninety participants were recruited from two centres (Birmingham and Newcastle upon Tyne, UK) comprising healthy, gingivitis, periodontitis, and edentulous donors. Samples from the Birmingham cohort were analysed by quantitative mass spectrometry proteomics for biomarker discovery. Shortlisted candidate proteins were then verified by enzyme‐linked immunosorbent assay in both cohorts. Leave‐one‐out cross validation logistic regression analysis was used to identify the best performing biomarker panels.
Results: Ninety‐five proteins were identified in both GCF and saliva samples, and 15 candidate proteins were selected based upon differences discovered between the donor groups. The best performing panels to distinguish between: health or gingivitis and periodontitis contained matrix metalloproteinase‐9 (MMP9), S100A8, alpha‐1‐acid glycoprotein (A1AGP), pyruvate kinase, and age (area under the curve [AUC] 0.970); health and gingivitis contained MMP9, S100A8, A1AGP, and pyruvate kinase, but not age (AUC 0.768); and mild to moderate and advanced periodontitis contained MMP9, S100A8, A1AGP, pyruvate kinase, and age (AUC 0.789).
Conclusions: Biomarker panels containing four proteins with and without age as a further parameter can distinguish between periodontal health and disease states.
Bibliographical noteThis is an open access article under the terms of the Creative Commons Attribution-Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
FunderThis work was supported by Philips Research. Helen J. Cooper is an EPSRC Established Career Fellow (EP/S002979/1).
- ORIGINAL ARTICLE
- ORIGINAL ARTICLES
- gingival crevicular fluid
- periodontal disease