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

Autor: Delor I, Charoin Je, Sylvie Retout, Jacqmin P, Ronald Gieschke
Rok vydání: 2013
Předmět:
Zdroj: CPT: Pharmacometrics & Systems Pharmacology
ISSN: 2163-8306
DOI: 10.1038/psp.2013.54
Popis: Alzheimer's disease (AD), the most common dementing illness in the elderly, is a major growing public health issue as life expectancy increases. AD is characterized by a slow decline in cognitive and functional ability assessed by various clinical, biochemical, imaging, and genetic biomarkers. However, the large variability in disease progression among individuals, from normal to prodromal (predementia), mild cognitive impairment (MCI), and dementia,1 hinders a correct prognosis of the disease and associated assessment of treatment effects of new disease-modifying drugs. An efficient way to comprehensively integrate and use the available information is through population disease progression modeling. In this context, the AD neuroimaging initiative (ADNI),2,3 an on-going longitudinal natural long-term history study of elderly designed to collect validated data such as MRI and PET images, cerebral spinal fluid and blood biomarkers together with clinical/cognitive measurements in normal subjects (NL), MCI, and AD is a first and important step in the early detection and tracking of AD. Different groups have already published disease progression models for cognitive deterioration based on ADNI data.4,5,6,7 These models, briefly summarized hereunder, are mainly based on the longitudinal response in AD assessment scale-cognitive subscale (ADAS-cog). On the basis of the mixed effects modeling approach, Ito3 developed a linear AD progression model throughout all populations (i.e., NL, MCI, and AD), in which ADAS-cog evolved over time with a constant progression rate. In this model, the progression rate was shown to be influenced by baseline ADAS-cog, age, apolipoprotein (APOE) e4 genotype, and sex. Samtani built separate nonlinear mixed effect models for AD4 or MCI5 populations using logistic models. In those models, ADAS-cog score deteriorated slowly during the early stage of the disease and more rapidly during the middle stage. Disease progression rates were shown to be mainly influenced by Trail B test, APOE e4 genotype and high cholesterol (or high p-tau181P/Aβ1-42 ratio), whereas high ADAS-cog baseline values were associated with atrophy of hippocampal volumes (HIPV). Moreover, based on ADAS-cog baseline levels and progression rates, Samtani's model identified two subpopulations of MCI patients (MCI progressers and nonprogressers) which correlated rather well with pathologic cerebrospinal fluid biomarker signature (high p-tau181p and low Aβ1-42) as reported by Meyer.8 Finally, Yang,6 being aware that the duration of clinical trials are too short to show significant changes in biomarkers, judiciously proposed to calculate a time shift for observed ADAS-cog scores across subjects and populations leading to an optimal fit of resulting scores to a theoretical curve of the disease progression. Subsequently, they mapped changes in biomarker data according to their new disease timeline. However, even if these analyses have brought significant advancement in quantitative understanding of disease progression and in the impact of important covariates, a better understanding of the evolution of individuals at the prodromal phase of AD, i.e., with presymptomatic or mild signs of dementia, remains crucial as they are a high-risk group likely to benefit from effective treatments. In that context, it appears that the clinical dementia rating scale sum of boxes (CDR-SOB) score could be a valuable candidate as outcome indicator in prodromal/MCI populations and facilitate assessment of active vs. placebo treatment. CDR-SOB is commonly used to asses both cognitive and functional impairment of AD. It has been shown to be less variable than ADAS-cog, to have excellent 2-year responsiveness and to be appropriate in distinguishing between MCI and AD patients.9 The objective of this work is to develop an original natural history population disease progression model based on CDR-SOB scores from ADNI by integrating the experiences from previous ADAS-cog modeling. The model presented herein, similarly to Yang's model,6 is based on the concept that study entry time does not correspond to the start of the disease as illustrated in Figure 1. Analyzing the data at the population level permits derivation of the most likely common disease trajectory using time scale adjustment and back- and forth extrapolation between AD and MCI patients. In addition, the different approaches developed by Samtani4,5 have been adapted to the CDR-SOB score. Figure 1 Scale synchronization on disease onset time. (a) Typical clinical dementia rating scale—sum of boxes score profiles in normal (NL), mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects from AD neuroimaging initiative. (b) Subpopulation ...
Databáze: OpenAIRE
Externí odkaz: