Concurrent Diabetes Mellitus and Periodontitis in U.S. Adults: Mortality Associations and Machine Learning Risk Prediction
Jincheng Li, Tian Zhu, Haruhiko KashiwazakiBackground/Objectives: We examined mortality associations by diabetes mellitus (DM)–periodontitis status and the incremental predictive value of composite inflammatory indices among adults with concurrent disease. Methods: We analyzed 10,632 adults from the National Health and Nutrition Examination Survey 2009–2014 linked to mortality data through 2019. Groups included neither DM nor periodontitis (NDM + NP), periodontitis alone (P), DM alone (DM), or concurrent DM and periodontitis (DM + P). Survey-weighted Cox and Fine–Gray models assessed all-cause and cardiovascular mortality. Among 978 participants with DM + P, nine survival models underwent five repeats of fivefold cross-validation to assess 5-year all-cause mortality prediction and the incremental value of eight composite inflammatory indices. Results: DM + P was associated with higher all-cause mortality than NDM + NP (HR, 1.51; 95% CI, 1.17–1.94) and P (HR, 1.31; 95% CI, 1.09–1.57). For cardiovascular mortality, DM + P was associated with higher mortality than NDM + NP (subdistribution hazard ratio [sHR], 1.51; 95% CI, 1.003–2.276), although the confidence interval only narrowly excluded 1.0. DM + P also had higher cardiovascular mortality than P (sHR, 1.55; 95% CI, 1.14–2.10). Neither outcome differed significantly between DM + P and DM. The Combined extreme gradient boosting Cox model had a concordance index and 5-year AUC of 0.754. Adding inflammatory indices did not significantly improve discrimination, prediction error, calibration, or net benefit. Observed 5-year mortality across predicted-risk tertiles was 3.7%, 7.4%, and 21.2%. Conclusions: Concurrent diabetes and periodontitis were associated with higher all-cause and cardiovascular mortality than periodontitis alone, but not with significantly higher mortality than diabetes alone. Composite inflammatory indices did not significantly improve prediction beyond clinical predictors.