Detection and Treatment of Gum Disease Possible with Genetic Classification
A new study published in the Journal of Dental Research has proposed a new way of before the condition turns severe. The researchers suggested a new system based on the genetic makeup of gum disease. This was different from the current method based on symptoms and clinical indicators.
The researchers said that the system allows earlier detection of the gum disease and make way for personalized treatment before bone and teeth loss occur. The current system classifies periodontal disease as aggressive or chronic based on the how much bone is lost and the condition of the gums.
Lead researcher Panos N. Papapanou, chair of oral and diagnostic sciences at the College of Dental Medicine at Columbia University Medical Center, had a problem with the current system. It doesn’t indicate when the gun infection is aggressive until the damage has become irreversible.
The researchers wanted to find a better way to classify gum disease and looked into modern ways cancer is diagnosed. At present, biologists have discovered markets for cancer responsiveness and aggressiveness within the genetic signatures of tumors. Their discoveries are used to classify and find appropriate treatment for patients.
In order to establish whether the same methodology will work for periodontal disease, the researchers made an analysis of diseased gum tissue from 120 patients who were diagnosed with either aggressive or chronic periodontitis. They discovered that patients who belonged to one of the two groups based on the genetic signature of the gum disease. They also discovered two groups that were not aligned with the two classes of the present system based on symptoms. They had differences in severity and extent of the disease.
The researchers want to do another study where they can use their classification system with a group of patients that they can follow in a period of time. They want to find out the effectiveness of the new system to predict the outcomes of the disease.
Photo by “Hanabishi”Hanabshi (“Hanabishi’s file”) [Public domain], undefined