Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers—including CSF Aβ42, Amyloid PET, CSF Tau, and MRI FDG-PET—were analyzed simultaneously to compute a Cognitive Impairment (CI) score, dynamically adjusted for age. Higher CSF Aβ42 levels consistently demonstrated a protective effect, while elevated Amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. The study highlights the model’s potential to identify individuals in risk zones, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Future work aims to validate the model using longitudinal real-world datasets and expand its predictive capacity through machine learning techniques and the integration of genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer’s Disease by providing a scalable computational tool for early risk assessment and intervention planning.