Data-driven models of dominantly-inherited Alzheimer’s disease progression

Abstract

See Li and Donohue (doi:) for a scientific commentary on this article.The key to developing interventions for Alzheimer’s disease may lie within the less common familial form, which has a predictable presymptomatic phase. Oxtoby et al. use modern computational techniques to characterize familial Alzheimer’s disease progression. The resulting data-driven models allow fine-grained patient staging, with potential utility in clinical trials.

Predicting time to dementia using a quantitative template of disease progression

“…It is difficult to compare the performance of our prediction with results reported in the literature based on previous methods given differences in samples and model features used. Oxtoby et al [5] reported an RMSE of 1.3 years for predicting age at onset over the course of 3.0 years in the context of dominantly inherited AD based on CSF, MRI, and PET biomarkers. Vogel et al [22] reported an value of 0.15 using the ADNI data set for predicting years to conversion to MCI or AD based on functional and structural MRI measures.…”

“…However, this is a challenging goal given that there is no scientific consensus regarding the definition of healthy aging. Several studies have approached this problem by including age and other demographic variables as covariates in addition to a disease stage variable [11,31,32] or by regressing out their effects before model fitting [5,33,34], whereas others have not included covariates to characterize trajectories that explain the natural history of AD dementia, including biological and cognitive changes that occur due to aging in addition to AD [4,6–9,13,15]. We formulated our goal with this study as the characterization of the natural history of AD dementia, including biological and cognitive changes that occur due to aging in addition to AD pathology, and therefore did not include an adjustment for age or any other demographic variables.…”

“…These models emphasize the interpretability of model results rather than optimization of predictive performance, where discriminative methods might outperform but produce results that are not as easily interpretable. Existing generative models of AD progression can be divided into two major categories based on the granularity of their characterization of the latent disease progression variable, either as a sequence of discrete events [3–5] or as a continuous variable [6–14]. In addition to characterizing changes in biomarker trajectories as a function of latent disease stages, these statistical models provide individualized information that can be used for personalized disease staging and monitoring.…”

Age‐dependent amyloid deposition is associated with white matter alterations in cognitively normal adults during the adult life span

“…The deposition of cortical amyloid β (Aβ; henceforth we refer only to cortical Aβ) precedes the onset of Alzheimer's disease (AD) symptoms by >20 years, 15,16 typically beginning within the fourth or fifth decade of life 17,18 . Aβ deposition has been associated previously with small vessel disease in the form of capillary cortical amyloid angiopathy (CAA) and arteriosclerosis, 6,19,20 which may entail white matter damage 6,21,22 .…”

Simultaneously evaluating the effect of baseline levels and longitudinal changes in disease biomarkers on cognition in dominantly inherited Alzheimer's disease

“…First, our analyses are based on an ADAD cohort, and our findings may not extend to the more common sporadic AD where the age of onset is much older and additional pathologies less common in ADAD (e.g., TDP‐43, cerebrovascular disease) might influence the associations identified here. Although these merit direct investigation, two recently published articles using a data‐driven, event‐based modeling technique identified a similar biomarker/cognition profile between ADAD and sporadic AD (initial change in Aβ, followed by CSF measures of tau/ptau, brain atrophy, and then cognitive decline) [31,32], thus providing evidences that the association between the biomarker longitudinal change and cognitive decline might be similar in sporadic AD. Ongoing studies in the Alzheimer Disease Neuroimaging Initiative and DIAN will provide the opportunity to directly compare cohorts that are followed with very similar procedures [33].…”