Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR‐SOB Scores From ADNI

Abstract

Disease-onset time (DOT) and disease trajectory concepts were applied to derive an Alzheimer's disease (AD) progression population model using the clinical dementia rating scale—sum of boxes (CDR-SOB) from the AD neuroimaging initiative (ADNI) database. The model enabled the estimation of a DOT and a disease trajectory for each patient. The model also allowed distinguishing fast and slow-progressing subpopulations according to the functional assessment questionnaire, normalized hippocampal volume, and CDR-SOB score at study entry. On the basis of these prognostic factors, 81% of the mild cognitive impairment (MCI) subjects could correctly be assigned to slow or fast progressers, and 77% of MCI to AD conversions could be predicted whereas the model described correctly 84% of the conversions. Finally, synchronization of the biomarker-time profiles on estimated individual DOT virtually expanded the population observation period from 3 to 8 years. DOT-disease trajectory model is a powerful approach that could be applied to many progressive diseases.

Instantiated mixed effects modeling of Alzheimer's disease markers

“…Detailed studies into early state longitudinal Alzheimer's disease marker trajectory dynamics, using data-driven methods, have the potential to aid the effort in the development of measures that can accurately and robustly quantify indications of the disease, even before its presymtomatic and preclinal stages. Previously, hypothetical [23,24] and experimental models [13,14,50,10,20,7,25,41,21,4,5,1,12,15,53] of disease progression based on Alzheimer's disease markers, such as cerebrospinal fluid (CSF), imaging and cognitive markers have been proposed.…”

“…Recently, several approaches that regard the disease progression trajectory as a continuous process have been developed [13,14,50,10,20,7,25,41,21,4,5,1,12]. Many of the proposed frameworks rely on modeling cognitive scores such as the mini mental state examination (MMSE), Alzheimer's disease assessment scale (ADAS), clinical dementia rating sum of boxes (CDRSB), or Rey's audio visual learning test (RAVLT) among others, as surrogate measures of disease progression.…”

“…Yang et al [50] proposed an exponential model of ADAS scores and used this to estimate the disease duration and current pathological stage of a patient. Similarly, Delor et al [10] computed a disease onset time by adjusting subjects according to CDRSB score. Ito et al [20] developed a model to describe the longitudinal response in ADAS score, where the rate of progression was found to increase with baseline severity, age and/or ApoE-4 genotype status.…”

A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data

“…However, in studies on neurodegenerative diseases, such as Alzheimer's disease, there may be no natural choice for the reference time. As pointed out by Yang et al in [4] and Delor et al in [3], the disease-onset time is probably different for every individual and, at a given age, two individuals may be at very different stages of disease progression. When the baseline (or disease-onset) time is unknown, one may consider it as a fixed effect of the given model.…”

“…In [2], the authors proposed an interesting mixed-effect model for scalar measurements but the model did not account for the difference in stages progression among individuals. Yang et al [4] and Delor et al [3] addressed this issue by including time shifts in their models but Yang et al did not estimate the time shifts in a statistical framework. In Delor et al, the observations of a given individual were shifted in time before the estimation of a disease-onset time.…”