Higher cognitive function scores were linked to lower all-cause mortality risk in elderly US adults with CKM syndrome, with the AI TabPFN model predicting mortality with AUC 0.75.
Does higher cognitive function predict lower all-cause mortality in US elderly adults with cardiovascular-kidney-metabolic syndrome?
Higher cognitive function is associated with reduced all-cause mortality in older US adults with CKM syndrome, and an AI-based model (TabPFN) can effectively predict this risk.
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Abstract Background Cardiovascular-kidney-metabolic (CKM) syndrome, characterized by progressive low-grade inflammation, is increasingly recognized as a critical determinant of adverse health outcomes in aging populations. However, the prognostic significance of cognitive function among the elderly population with non-advanced CKM (stages 0-2) and advanced CKM (stages 3-4) remains poorly defined, limiting targeted interventions for this high-risk population. Purpose To examine the association between cognitive function and all-cause mortality among individuals with CKM, and to develop a novel artificial intelligence model for early cognitive screening and intervention in this high-risk population. Methods This study utilized data from the National Health and Nutrition Examination Survey (2011–2014) to evaluate cognitive function in 1155 US older adults (aged ≥60 years) with complete cardiovascular-kidney-metabolic (CKM) data. Cognitive function was evaluated using neuropsychological assessment batteries. Mortality records were obtained through linkage with the National Center for Health Statistics. Weighted regression models were employed to examine the relationship between cognitive function and mortality, while subgroup analyses explored mortality risks associated with cognitive scores. The Tabular Prior-Data Fitted Network (TabPFN) was selected for its superior predictive performance and was used to develop a novel screening and early intervention tool that integrates cognitive scales with CKM stages. Results During an average follow-up period of 79 months, there were 197 all-cause deaths. After adjusting for confounders, weighted regression analysis showed that higher cognitive function scores were associated with lower all-cause mortality risk in both advanced and non-advanced CKM groups among older adults (all HR 1, P 0.05). Consistent trends were observed across all subgroups. Smoking and obesity significantly interacted with cognitive function in influencing the risk of all-cause mortality. The receiver operating characteristic curve showed that TabPFN model, based on cognitive function and CKM stages, had highest predictive value (AUC= 0.75, 95%CI: 0.71-0.78). Conclusion Maintaining good cognitive health is of significant importance for the prevention of all-cause mortality risk among US elderly individuals with either advanced or non-advanced CKM syndrome. Integrating neuropsychological assessment batteries with the TabPFN model could represent the optimal approach for early cognitive screening and intervention in managing CKM syndrome among the elderly US population.
Lin et al. (Sat,) reported a other. Higher cognitive function scores were linked to lower all-cause mortality risk in elderly US adults with CKM syndrome, with the AI TabPFN model predicting mortality with AUC 0.75.