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.

AIM: To 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 METHODS: GCF 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.

RESULTS: A 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.

CONCLUSIONS: The 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.