An Accessible Predictive Model for Alzheimer’s Disease Based on Cognitive and Neuropathological Integration

Alzheimer’s disease is a progressive neurodegenerative disease and a major public health concern globally in aging populations. Currently, there are limited treatments for this disease, and it not only causes irreversible cognitive decline but also imposes burdens on patients’ families, caregivers, and healthcare systems. While its pathology and mechanisms have been widely studied, early identification of individuals at risk remains challenging. Conventional screening often relies on categorical thresholds, which might overlook subtler and continuous changes in cognitive functions and behaviors. This research aims to develop a predictive model that quantifies an individual’s risk of Alzheimer’s disease by using cognitive scores, declining trends, demographic information, and neuropathological markers, and to identify clinically interpretable risk factors. Based on the SEA-AD cohort, the researchers constructed a logistic regression model by incorporating 10 continuous variables, including four cognitive test scores, their decline slopes, age, years of education, sex, and 11 categorical variables derived from neuropathology, such as LATE and CAA, selected through correlation analysis and clinical relevance. Specifically, for selecting relevant variables, we constructed heatmaps, demonstrating all the correlations of data and finding the strongest associated ones with AD diagnosis. The model demonstrates strong discriminatory performance (AUC = 0.96). LATE Stage 3, severe arteriolosclerosis, and memory decline were associated with increased AD risk, while higher baseline memory, language test scores, and more years of education could delay the onset of dementia, as the data show. To illustrate the model’s interpretability, we conducted a hypothetical case simulation by simulating a 75-year-old male with mild cognitive impairment and no advanced pathology, yielding a predicted AD risk of 6.3%. This model demonstrates the potential for AD identification at an early age, providing a tool for individuals to test their risk of AD by using certain clinical scores and data.