Graduate student works to find biomarkers for stroke risk prevalence

Strokes occur every 40 seconds in the U.S., meaning that almost 800,000 people have a stroke in the country every year. However, up to 80% can be prevented by paying attention to risk factors, according to the U.S. Center for Disease Prevention and Control.

Richard and Loan Hill Department of Biomedical Engineering PhD student Haritha George received a prestigious student travel award for the recent Institute of Electrical and Electronics Engineers (IEEE)-Engineering in Medicine and Biology Science (EMBS) International Conference. The conference focused on Body Sensor Networks NextGen Health: Sensor Innovation, AI, and Social Responsibility. It took place in Chicago in mid-October.

Along with Professor Mathew Mathew, George is investigating the possibility of a point-of-care device to predict the risk prevalence of strokes. The preliminary data is a promising proof-of-concept study. They hope to scale down the device from something that sits on a tabletop to handheld sized.

The device uses an electrochemical sensor to screen body fluids, such as saliva, for biomarkers that indicate a biological state or condition. These could be proteins that could detect the presence of any disease. George said that the research she presented at the conference focuses on stroke risk prediction. However, this comes with challenges, as there are no specific biomarkers that can predict when a stroke could happen.

She also noted that there are lifestyle choices that people can make to lower their risk factors for and even prevent strokes and other diseases. These choices include monitoring blood pressure, cholesterol, and blood sugars, eating a healthy diet, exercising regularly, quitting smoking, and getting enough sleep.

When looking at the reduced disease risks associated with these lifestyle choices, George and her team saw that in all the associated diseases that could lower stroke risk, as mentioned by the CDC, the underlying factor is when there are high levels of reactive oxygen species (ROS), it can contribute to high oxidative stress in the body, increasing the likelihood of a stroke. ROS are unstable molecules that contain oxygen and are highly reactive with other molecules in cells. This could activate downstream stress pathways and lead to cellular damage or cell death.

“In the case of strokes, if we can find biomolecules that can act as markers for cellular stress level detection, then we could advise patients that the oxidative stress in the body is high, so they need to take care of their health,” George said. “That was the thought behind finding stroke risk prediction instead of stroke prediction because going into the neuroscience domain for diagnostic stroke biomarkers is a bit risky because there are a lot of uncertainties, and a lot is still confusing at the molecular level that could eventually lead to a stroke.”

She added that they also thought the MMP 9 molecule, an enzyme that breaks peptide bonds, was responsible for breaking the cellular wall. This molecule is relevant in all disease risk factors for stroke and could be used as a predictive marker. They used the concentration levels of the MMP9 molecule, or matrix metalloproteinase-9 molecular together with the antioxidant glutathione molecule, from the case-control studies to make risk tables of the concentrations of healthy people and those who had a stroke. These two molecules could give an approximate overview of the person’s oxidative stress.

When the saliva was tested for these two molecules electrochemically using the antibody for the specific biomarker as bait, five electrochemical parameters per molecule/biomarker were collected. The five parameters included constant phase element, change in impedance, solution resistance, polarization resistance, and cyclic voltammetry, together with the risk table given to a machine learning model for the risk prediction when they implemented machine learning in their research. In using the salivary biomarker, electrochemistry, and machine learning, those at risk for stroke, especially in recurrent stroke cases in the first year after the initial stroke, can test easily at home and get results quickly, if this technology is successfully developed.

George plans to complete her PhD and graduate in the summer of 2025. Her thesis focuses on intelligent salivary biomarkers for systemic diseases, including stroke risk. She works in Mathew’s Regenerative Medicine and Disability Research Lab at the UIC College of Medicine Rockford. Mathew is her mentor, and Russel Pesavento, UIC College of Dentistry assistant professor, is her co-mentor.

They also worked with research collaborators Xue-June Li of (Biomedical Science, UICOM), Praven Genjedraredy (UIC Dental School), Dr. Yan Yan, Department of Computer Science, and Krisha Veeravalli (UIC Peoria).

They acknowledge the financial support from NIH-R01 grant, NIH- 1R01DE031832, where Dr. Mathew and Dr. Wang (WPI, Boston) are the multi-PIs. The Office of Technological Management and UIC (Dr. Melissa and Charles Frisbie) supported the development of the technology and disclosed it at UIC.

George added she was very grateful and honored to be one of the recipients of this prestigious student travel award from IEEE BSN and to present her preliminary data.