Risk Prediction of Edentulism in Chinese Adults: Insights From the China Health and Retirement Longitudinal Study (CHARLS).

BACKGROUND

Edentulism concentrates in older adults and is associated with poorer diet, elevated malnutrition risk, and adverse health trajectories. Interpretable, well-calibrated tools applicable in routine care are needed to identify those at risk and support timely, tooth-preserving management.

METHODS

We analyzed data from the nationally representative China Health and Retirement Longitudinal Study with baseline in 2011 to 2012 and follow-ups in 2013, 2015, and 2018. Adults with baseline dentition status and all prespecified predictors were eligible. Incident edentulism was ascertained at each wave. Predictors defined a priori included age, sex, body mass index, blood pressure, lipid profile, log-transformed C-reactive protein (ln-CRP), and estimated glucose disposal rate (eGDR). Four algorithms were trained in a development set and evaluated in an independent validation set: multivariable logistic regression, LASSO-penalized logistic regression, random forest, and extreme gradient boosting. Validation included area under the receiver-operating-characteristic curve (AUC) with nonparametric comparisons, calibration with logistic recalibration, decision-curve analysis across clinically relevant thresholds, and time-dependent AUC over follow-up.

RESULTS

Of 6130 adults, 5231 were dentate at baseline and formed the risk set; incident edentulism occurred in 2013 (n = 338), 2015 (n = 253), and 2018 (n = 308). Logistic regression yielded the highest discrimination (AUC 0.695) with stable performance across waves and over 2-7 years. Calibration closely matched the ideal after simple recalibration, and decision curves showed consistent net benefit versus treat-all and treat-none strategies. In the final model, age and C-reactive protein (ln-CRP) were dominant independent predictors; men had lower risk, and total cholesterol showed a modest inverse association. A nomogram was derived to enable point-of-care risk stratification.

CONCLUSIONS

In a large, nationally representative cohort, an internally validated logistic regression model based on routinely available data showed moderate discrimination, good calibration, and potential clinical utility for predicting incident edentulism. Age and low-grade systemic inflammation were the main contributors to risk stratification and may help guide early, tooth-preserving care.