Poster W2 - Stroke Risk Prediction and Monitoring with Salivary Biomarkers Matrix Metallo Proteinase 9 and Glutathione Using Electrochemical Biosensor: A Proof-of-Concept Study

Introduction: Oral health as an indicator of systemic health is becoming increasingly relevant, with many diseases like periodontitis linked to systemic diseases. According to the CDC, every 40 seconds, someone in the United States has a stroke. Saliva has molecules indicative of the disease condition, making the salivary biomarkers an up-and-coming field of early diagnosis. This study considers periodontitis biomarker Matrix Metallo Proteinase 9 (MMP9) and oxidative stress biomarker Glutathione (GSH) to be used for early stroke risk prediction. MMP9 is a Zn2+-dependent endopeptidase, and an increase in MMP-9 levels is seen following stroke. GSH is an intracellular thiol tripeptide in all mammalian tissues that plays a crucial role in cellular protection against oxidant damage during stroke.

Materials and

Methods: An optimum concentration of MMP9 and GSH antibody to be coated on the biosensor surface to functionalize the electrode surface was found by antibody saturation experiments. Electrochemical analysis of artificial saliva was done independently with clinically relevant concentrations of MMP9 protein and GSH protein. Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV) were conducted. From the EIS data, the change in impedance and percentage change in capacitance and charge transfer (CV) area was calculated for each test condition. ELISA and confocal Imaging were done to check MMP9 and GSH antibody-antigen interaction. The electrochemical properties obtained are used as Machine Learning (ML) data input, and the prediction of risk levels of stroke was done with different ML models. The antioxidant effect of GSH on MMP9 was also checked electrochemically.

Results, Conclusions, and Discussions:

Results: From the electrochemical studies, the change in impedance decreased with MMP9 concentration, and for GSH, it decreased to 750ng/ml and then increased. Percentage change in capacitance decreased with MMP9 concentration; GSH decreased till 750ng/ml and then increased. CV area increased for MMP9 and decreased for GSH as their concentration increased. The SEM-EDS study showed that antibodies are attached to the biosensor, enabling protein detection. Confocal Imaging percentage fluorescence analysis with Image J software indicated an increasing percentage of fluorescence with increasing protein concentration. ELISA absorbance reading also increased with increasing protein concentration, and both enabled biological characterization of antibody and protein interaction. ML-based stroke risk prediction using SVM, SVM with RBF kernel and NN was done as normal, low risk, and high risk with an accuracy of 83.33%, 65.59%, and 62.5% for MMP9 and 75%, 73.14%, and 75.44% for GSH. Also, the antioxidant effect of GSH can be seen, as the electrochemical trend followed GSH in lower concentrations of MMP9 protein in combination studies.

Discussion: This study examined the possibility of knowing stroke risk using salivary biomarkers. The detection of MMP9 and GSH by electrochemical biosensors indicates the potential to use them as electrochemical biomarkers for early stroke risk prediction and the data for ML-driven prediction tools for stroke risk. GSH, with its antioxidant effect, might reduce the MMP9 effects until the MMP9 level increases to a certain extent. The pathways involved will also be checked in future studies to determine the alterations of MMP9 and GSH in patients’ saliva samples and cellular level changes to MMP9 in the presence of GSH.

Conclusions: Even with that alarming number of strokes, the CDC mentions that 80% of strokes can be prevented. So, this biosensor-based detection will enable a chance to prevent stroke by knowing the risk early on by a noninvasive salivary diagnosis. The test results could prompt changes in lifestyle and habits to prevent stroke risk factors like diabetes, high cholesterol, and obesity from happening and thereby reduce the occurrence or severity of the stroke.

Acknowledgements (Optional): NIH, Blazer Foundation, UIC BME, UICR, UIC-COD, IIT. Funding- NIH RO1 DE031832.