Abstract
AIM: To develop and validate a predictive model for moderate-to-severe periodontitis in the adult USA population, with data from the 2011-2012 National Health and Nutrition Examination Survey (NHANES) cycle. MATERIAL AND METHODS: A subset of 3017 subjects aged >30 years, with >14 teeth present and having received a periodontal examination in addition to data collected on cardio-metabolic risk measures (smoking habit, body mass index [BMI], blood pressure, total cholesterol and glycated haemoglobin [HbA1c]) were used for model development by multivariable logistic regression. RESULTS: The prevalence of moderate and severe periodontitis using CDC/AAP classification was 37.1% and 13.2%, respectively. A multivariable logistic regression model revealed that HbA1c ≥5.7% was significantly associated with moderate-to-severe periodontitis (odds ratio, OR = 1.29; p < 0.01). A predictive model including age, gender, ethnicity, HbA1c and smoking habit as variables had 70.0% sensitivity and 67.6% specificity in detecting moderate-to-severe periodontitis in US adults. CONCLUSIONS: Periodontitis is a common disease in North American adults, and its prevalence is significantly higher in individuals with pre-diabetes or diabetes. The present study demonstrates that a model including age, gender, ethnicity, HbA1c and smoking habit could be used as a reliable screening tool for periodontitis in primary medical care settings to facilitate referral of patients at risk for periodontal examination and diagnosis.