Background
Alzheimer’s disease (AD) is an irreversible complex neurodegenerative disorder characterized by amyloid plaque accumulation. Amyloid plaque is a critical hallmark of AD and is identified as one of the early-changing Core 1 biomarkers (Jack et al., 2024) (1) which map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). The objective of our analysis is to leverage machine learning (ML) coupled with disease progression (DP) models to screen many predictors for amyloid accumulation in the ADNI database. Early detection of risk factors could greatly reduce AD incidence and is specifically useful in primary prevention (PP).
Methods
A natural amyloid accumulation DP model (2) identified baseline factors of age, gender, APOE4 genotype and disease state as important factors and the model was used to simulate virtual subjects based on 1291 ADNI participants. Thirty-seven covariates were screened in 100 simulated datasets using nine different ML methods as well as the traditional covariate selection methods (SCM, COSSAC, COSSAC/SAMBA) with and without accounting for highly correlated covariates. The performance of the methods was assessed using the F1 score (accuracy measure). The best method was then used to prescreen covariates in the original dataset followed by SCM.
Results
The ML AIC stepwise (AIC) method was the best method to screen the covariates on the amyloid baseline and slope even when correlated covariates were not accounted for (F1 score = 0.97 ± 0.08, & 0.52 ± 0. 09, respectively). The accuracy of AIC method improved after accounting of correlated covariates (F1 score = 1 ± 0, & 0.76 ± 0.25, respectively). SCM and COSSAC showed lower performance (F1 score = 0.82 ± 0.17/ 0.99 ± 0.025, & 0.68 ± 0.17/ 0.96 ± 0.097, without/with correlations respectively). Importantly, AIC runs had a lower computational burden compared to COSSAC and SCM (1.2 min ± 0.03, 37.6 min ± 26 & 6.84 hr ± 0.1 respectively). AIC as prescreening tool on the original dataset reduced the run time of COSSAC and SCM by 61% & 72.7% respectively. Further evaluation of the selected covariates led to an updated disease progression model with the effect of CSF amyloid-β 42 and phospho-tau on amyloid baseline and APOE4 genotype effect on amyloid progression.
Conclusions
ML methods ease computation and accelerate covariate screening. Simulations guide the choice of ML method and covariate selection. The updated DP model can aid in designing AD PP trials by targeting high-risk groups and quantifying disease progression.
References
- Jack Jr CR, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, Hansson O, Ho C, Jagust W, McDade E, Molinuevo JL. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimer’s & Dementia. 2024 Aug;20(8):5143-69.
- Elhefnawy ME, Patson N, Mouksassi S, Pillai G, Shcherbinin S, Chigutsa E, Gueorguieva I. Quantifying natural amyloid plaque accumulation in the continuum of Alzheimer’s disease using ADNI. Journal of Pharmacokinetics and Pharmacodynamics. 2025 Feb;52(1):15.
