New EIOH Research Shows Promise for Predicting Early Childhood Caries

New data indicates two significant findings: The ability to predict Early Childhood Caries based on the type of bacteria in the child’s mouth, and how stress may have a role in the onset of ECC.

Over the last five years, Dr. Kopycka-Kedzierawski, professor of Dentistry at Eastman Institute for Oral Health, and her team have been studying how oral microbiology, family functioning, and stress may lead to ECC, severe tooth decay that disproportionately affects children living in poverty and is a significant public health problem. ECC often leads to pain, infection, loss of sleep, speech problems, loss of appetite, difficulty sleeping and can negatively affect a child’s ability to learn.

Two-hundred children between one and three years of age who were at risk for ECC but initially cavity-free were enrolled in the NIDCR/NIH funded study and followed for two years. At six-month intervals the research team completed an oral health exam and collected detailed information on nutrition, oral microbiology, and child stress exposures.

The first paper reports that there is wide variation in the bacteria and yeast that cause caries and that children from low socio-economic settings are more likely to carry these bacteria and yeast. They also found that one of those biological causes of caries, Mutans Streptococcus (MS), was also higher in children whose cortisol levels suggested that they had experienced significant stress.

“The findings provide valuable and novel information that, pre-ECC onset, the caries disease process is explicable from a detailed assessment of behavioral, socio-demographic, and psychosocial stress variables,” said Dr. KopyckaKedzierawski. These findings appear in the Journal of Dental Research-Clinical and Translational Research.

The second paper, which was recently published in the Journal of Dental Research, describes a novel approach to examine the possibility of predicting who will eventually develop caries from children’s oral bacteria content. They modeled caries risk in initially caries-free children using 16S ribosomal RNA gene sequencing and applied machine learning models. Oral bacteria found in children’s mouths --Rothia mucilaginosa, Streptococcus species and Veillonella parvula--were selected as important discriminatory features in all models and represent biomarkers of risk for ECC onset.

“We were able to predict who would get caries with almost 90% accuracy,” Dr. Kopycka-Kedzierawski explained. “Saliva microbiota profiling coupled with machine learning represents a promising approach to accurate, reliable and robust Caries Risk Assessment, and could lead to valuable prevention approaches.”

In addition to several dental researchers and an immunologist/microbiologist, Dr. Kopycka-Kedzierawski’s team includes UR School of Medicine and Dentistry Psychiatry Professor Thomas O’Connor, director of URMC’s Wynne Center for Family Research. ♦