Investigating the causal network of dementia by employing a causal discovery approach combined with natural language processing models

Abstract INTRODUCTION Comprehensively studying modifiable risk factors altogether to explore how they contribute to dementia mechanism is imperative for effective interventions. METHODS This study utilized natural language processing (NLP) models to pre-select candidate risk factors of dementia from 5,505 variables in the UK Biobank. We then took a holistic machine learning approach, fast causal inference in combination with mixed graphical models, to explore the complex causal mechanisms underlying dementia from 120 imputed variables. RESULTS The identified causal network highlighted eight risk factors which may directly or indirectly contribute to dementia. In particular, mental disorders due to brain damage and dysfunction and to physical disease were identified as direct causes as well as mediators on the causal pathways to dementia. Evidence for a direct causal impact of phenotypic age on dementia was less pronounced. DISCUSSION Our study offered valuable insights into the mechanisms of dementia. Beyond direct connections to nerve or brain disorders, the potential direct link with biological age highlights its possible value in dementia management. Moreover, the use of NLP models for variable pre-selection introduced an innovative application to medical research. Our study added weight to the accruing evidence that machine learning has a promising future for exploring complex disease mechanisms from high-dimensional data.