Penn State researchers link saliva tests to concussions

21 November 2017 (Last Updated November 21st, 2017 11:41)

Researchers at Penn State College of Medicine in the US have demonstrated a method to relate the small molecules present in saliva for the diagnosis and prediction of duration of concussions in children.

Penn State researchers link saliva tests to concussions
Saliva tests could potentially aid concussion patients. Credit: Penn State Health.

Researchers at Penn State College of Medicine in the US have demonstrated a method to relate the small molecules present in saliva for the diagnosis and prediction of duration of concussions in children.

The research team found that the measurement of certain microRNAs levels in the saliva of patients could aid in the determination of the length of concussion symptoms with 85% accuracy.

Penn State College of Medicine paediatrics assistant professor Dr Steven Hicks said: “There’s been a big push recently to find more objective markers that a concussion has occurred, instead of relying simply on patient surveys.

“Previous research has focused on proteins, but this approach is limited because proteins have a hard time crossing the blood-brain barrier.

“What’s novel about this study is we looked at microRNAs instead of proteins, and we decided to look in saliva rather than blood.”

The team isolated five microRNAs after analysing the levels of different molecules in the saliva samples collected from 52 concussion patients aged 7-21 years.

“What’s novel about this study is we looked at microRNAs instead of proteins, and we decided to look in saliva rather than blood.”

During the study, the microRNA profiles were compared to the patient symptoms during initial and follow-up evaluation of the subjects carried out using the Sport Concussion Assessment Tool (SCAT-3).

Parents of the participants were also consulted to obtain information on their children’s symptoms.

Following four-week assessments, the researchers observed that the five isolated microRNAs could correctly predict the duration of symptoms for 42 of 50 patients.

It is expected that such early prediction could help doctors in delivering the right care by prescribing medication earlier without waiting to see if symptoms clear up.