SimulAD: a dynamical model for personalized simulation and disease staging in Alzheimer's disease

SimulAD is a computational disease progression model (DPM) originally developed on the ADNI database to simulate the evolution of clinical and imaging markers characteristic of AD, and to quantify the dis-ease severity (DS) of a subject. In this work, we assessed the validity of this estimated DS, as well as the generalization of the DPM., by applying SimulAD on a new cohort from the Geneva Memory Center (GMC). The differences between the estimated DS of healthy, mild cognitive impairment and AD demen-tia groups were statistically significant (p-values < 0.05; d >= 0.8). DS correlated with MMSE (rho =-0.55), hippocampal atrophy (rho =-0.62), glucose hypometabolism (rho =-0.67), amyloid burden (rho = 0.31) and tau deposition (rho = 0.62) (p-values < 0.01). Based on the dynamics estimated on the ADNI cohort, we simulated a DPM for the subjects of the GMC cohort. The difference between the temporal evolution of similar biomarkers simulated on the ADNI and GMC cohorts remained below 10%. This study illustrates the robustness and good generalization of SimulAD, highlighting its potential for clinical and pharmaceu-tical studies.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This study assessed the validity and generalization of the computational disease progression model SimulAD on a new cohort, and found that it accurately estimated disease severity and had good generalization.