Predictability of oral disease progression across the life course:A 21-42-year longitudinal study using routinely...

To assess the predictability of long- and medium-term oral disease progression over 21-42 years using clinical and radiographic data from Dutch military personnel. Outcomes included increments in tooth loss, severe bone loss, DMFT, endodontically treated teeth (ETT), and combined outcomes.

Records of 198 military personnel with >20 years of follow-up were analyzed. Prediction models were developed for long-term risk assessment using limited cross-sectional baseline information, and for medium-term risk assessment using more detailed longitudinal clinical history. Long-term predictors included demographic variables (year of birth, sex, military rank and smoking) and caries-related variables (DT, MT, FT and a proxy for DT). Medium-term predictors included patient characteristics, dental procedures, and radiographic findings. LASSO regression was applied to identify key predictors. Model performance was assessed using ROC curves with AUC values corrected for optimism by 500-fold bootstrapping.

The study population consisted of 198 military personnel (predominantly male) with birth years ranging from 1958 to 1973, representing a broad age range and both military rank categories. Most outcomes showed moderate predictability, with AUC values ranging from 0.705-0.724 for long-term models and 0.609-0.676 for medium-term models. In long-term models, all outcomes were predictable to some extent. In medium-term models, only extractions (≥1), DMFT increment (≥1), and their combination were predictable. Year of birth, smoking, and military rank were consistent predictors in long-term models, whereas highest caries score and radiographic bone loss progression were most influential in medium-term models.

Oral disease progression can be predicted to a moderate extent using routinely collected clinical data, with better performance for long-term than for medium-term models. Prediction is mainly driven by prior disease experience and lifestyle-related factors and may support risk stratification and targeted prevention.

Routinely collected dental data enable moderate prediction of oral disease progression, supporting earlier identification of high-risk individuals and more tailored preventive care.