Graph neural network-based prediction of all-cause and cardiovascular mortality using periodontal site-level data from National Health and Nutrition Examination Survey (NHANES).

Background/purpose: Periodontitis is a common chronic inflammatory disease linked to systemic conditions. We applied a graph convolutional network (GCN) to site-level periodontal data to predict all-cause and cardiovascular mortality from National Health and Nutrition Examination Survey (NHANES). Materials and methods: Adults aged ≥30 years with full-mouth periodontal exams and linked mortality data through December 31, 2019 were included. Periodontal probing depth and clinical loss of attachment were measured. Each chart was converted into a graph with 168 nodes and anatomically defined edges. Graph-level embeddings were combined with age and sex to predict mortality. Model performance was evaluated in an independent test set using receiver operating characteristic - area under the curve (ROC AUC) and precision-recall - area under the curve (PR AUC). Results: Among 9034 participants (1000 deaths), deceased individuals had significantly greater mean probing depth (1.71 ± 0.68 mm vs. 1.58 ± 0.66 mm) and loss of attachment (2.46 ± 1.34 mm vs. 1.86 ± 1.06 mm) than survivors (both Conclusion: A GCN applied to site-level periodontal data achieved strong discrimination in predicting mortality. This finding highlights the prognostic significance of periodontal health and demonstrates the potential of graph-based deep learning for modeling complex periodontal-systemic interactions.