ABSTRACT
BACKGROUND: Alzheimer's disease (AD) prevention research requires methods for measurement of disease progression not yet revealed by symptoms. Preferably, such measurement should encompass multiple disease markers. OBJECTIVES: Evaluate an item response theory (IRT) model-based latent variable Alzheimer Progression Score (APS) that uses multi-modal disease markers to estimate pre-clinical disease progression. DESIGN: Estimate APS scores in the BIOCARD observational study, and in the parallel PREVENT-AD Cohort and its sister INTREPAD placebo-controlled prevention trial. Use BIOCARD data to evaluate whether baseline and early APS trajectory predict later progression to MCI/dementia. Similarly, use longitudinal PREVENT-AD data to assess test measurement invariance over time. Further, assess portability of the PREVENT-AD IRT model to baseline INTREPAD data, and explore model changes when CSF markers are added or withdrawn. SETTING: BIOCARD was established in 1995 and participants were followed up to 20 years in Baltimore, USA. The PREVENT-AD and INTREPAD trial cohorts were established between 2011-2015 in Montreal, Canada, using nearly identical entry criteria to enroll high-risk cognitively normal persons aged 60+ then followed for several years. PARTICIPANTS: 349 cognitively normal, primarily middle-aged participants in BIOCARD, 125 high-risk participants aged 60+ in PREVENT-AD, and 217 similar subjects in INTREPAD. 106 INTREPAD participants donated up to four serial CSF samples. MEASUREMENTS: Global cognitive assessment and multiple structural, functional, and diffusion MRI metrics, sensori-neural tests, and CSF concentrations of tau, Aß42 and their ratio. RESULTS: Both baseline values and early slope of APS scores in BIOCARD predicted later progression to MCI or AD. Presence of CSF variables strongly improved such prediction. A similarly derived APS in PREVENT-AD showed measurement invariance over time and portability to the parallel INTREPAD sample. CONCLUSIONS: An IRT-based APS can summarize multimodal information to provide a longitudinal measure of pre-clinical AD progression, and holds promise as an outcome for AD prevention trials.
ABSTRACT
We describe events spanning over 20 years that have shaped our approach to identification of interventions that may delay symptoms in Alzheimer's disease (AD). These events motivated the development of a new Centre for Studies on Prevention of AD that includes an observational cohort of cognitively normal high-risk persons and INTREPAD, a nested two-year randomized placebo-controlled trial of the non-steroidal anti-inflammatory drug naproxen sodium. INTREPAD enrolled 217 persons and will follow 160 in a modified intent-to-treat analysis of persons who remained on-protocol through at least one follow-up evaluation. The trial employs dual endpoints: 1) a composite global cognitive score generated by a battery of 12 psychometric tests organized into five subscales; and 2) a summary Alzheimer's Progression Score derived from latent variable modeling of multiple biomarker data from several modalities. The dual endpoints will be analyzed by consideration of their joint probability under the null hypothesis of no treatment effect, after allowing appropriately for their lack of independence. We suggest that such an approach can be used economically to generate preliminary data regarding the efficacy of potential prevention strategies, thereby increasing the chances of finding one or more interventions that successfully prevent symptoms.
ABSTRACT
BACKGROUND: Brain beta-amyloid status portends different trajectories of clinical decline. OBJECTIVE: Determine trajectories and predictive baseline variable(s). DESIGN: Longitudinal, up to 24 months. SETTING: ADNI sites. PARTICIPANTS: Healthy control (n=325), early and late mild cognitive impairment (n=279; n=372), and Alzheimer's dementia (n=216) subjects from ADNI-1/GO/2. MEASUREMENTS: Baseline amyloid status was based on first available CSF Aß1-42 or, [11C]PiB or [18F]florbetapir (FBP) PET. Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13) and Functional Activities Questionnaire (FAQ) were co-analyzed using Growth Mixture Modeling (GMM) to define latent class trajectories for each amyloid group. Classification and Regression Tree (CART) analysis determined which variables best predicted trajectory class membership using a number of variables available to clinicians. RESULTS: GMMs found two trajectory classes (C1, C2) each for amyloid-positive (P; n=722) and negative (N; n=470) groups. Most (90%) in the negative group were C2N with mildly impaired baseline ADAS-Cog13, normal FAQ and nonprogression; 10% were C1N with moderately impaired baseline FAQ and ADAS-Cog13 and trajectory of moderately worsening scores on the FAQ. C1P (26%) had more impaired baseline FAQ and ADAS-Cog13 than C2P (74%) and a steeper declining trajectory. CART yielded 4 decision nodes (FAQ <10.5, FAQ <6.5, MMSE ≥26.5, age <75.5) in positive and 1 node (FAQ <6.5) in negative groups, with 91.4% and 92.8% accuracy for class assignments, respectively. CONCLUSIONS: The trajectory pattern of greater decline in amyloid positive subjects was predicted by greater baseline impairment of cognition and function. While most amyloid-negative subjects had nonprogression irrespective of their diagnosis, a subgroup declined similarly to the gradually declining amyloid-positive group. CART predicted likely trajectory class, with known amyloid status, using variables accessible in a clinical setting, but needs replication.
