Abstract
Aim
To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque-induced periodontal disease states.
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.
To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque-induced periodontal disease states.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 622-632 |
| Number of pages | 11 |
| Journal | Journal of Clinical Periodontology |
| Volume | 49 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 22 Apr 2022 |
Bibliographical note
Funding Information:This work was supported by Philips Research. Helen J. Cooper is an EPSRC Established Career Fellow (EP/S002979/1). Funding information
Publisher Copyright:
© 2022 The Authors. Journal of Clinical Periodontology published by John Wiley & Sons Ltd.
Keywords
- biomarker
- gingival crevicular fluid
- periodontal disease
- proteomics
- saliva
ASJC Scopus subject areas
- Periodontics