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Prediction of dementia-related mortality in metabolic dysfunction-associated steatotic liver disease using survival
Basile Njei1,2,3,4,5,6, Sarpong Boateng1, Solomon Gyabaah7
1Section of Digestive Diseases, Department of Medicine, Yale University, New Haven, CT, USA.
Background:
Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized as a multisystem condition with extrahepatic complications, including cognitive decline, Alzheimer's disease, and related dementias. However, conventional clinical tools have limited ability to predict dementia-related mortality in this population.
Objective:
To develop and internally validate survival-based machine-learning models for predicting dementia-related mortality among adults with MASLD and identify key clinical predictors.
Methods:
Adults with MASLD were identified from NHANES III with linked mortality data. Dementia-related mortality was defined using National Death Index codes for Alzheimer's disease and other dementias. Cox proportional hazards models, random survival forests, gradient-boosted survival models, and logistic regression were developed and compared with exploratory comparator model. Performance was evaluated using concordance indices, area under the receiver-operating-characteristic curve, Brier scores, and reclassification metrics relative to the FIB-4 score.
Results:
Among 1774 adults with MASLD, 115 dementia-related deaths occurred during a median follow-up of 193 months. Overall discrimination was modest. Logistic regression showed the highest discrimination in 5-fold cross-validation, whereas the penalized Cox model achieved the highest bootstrap C-index. The gradient-boosted survival model demonstrated the greatest improvement in reclassification compared with FIB-4. Exploratory risk stratification classified all dementia-related deaths within the high-risk cohort, although these findings warrant cautious interpretation due to possible overfitting. Waist circumference, diabetes, body mass index, and age were the most influential predictors.
Conclusions:
Survival-based models may improve identification of MASLD patients at elevated risk of dementia-related mortality. Metabolic and anthropometric factors appeared more informative than liver fibrosis scores, and warrant external validation in contemporary cohorts.