ALZEHEIMERS DISEASE NEUROIMAGING PROTOCOL (ADNI)

点击翻译按钮获取中文摘要

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. WHY ADNI? At present, the development of drugs for patients with AD is costly and requires a considerable length of time. Currently marketed drugs have been developed for symptomatic treatment of AD and trials can be completed in 6 months. Trials designed to slow the rate of decline necessary to demonstrate disease modification require at least one year of treatment or longer to see adequate clinical separation of groups. The development of drugs for subjects with mild cognitive impairment (MCI) takes longer since these subjects progress more slowly. Current MCI trials require 3-4 years to establish either a sufficient rate of clinical decline or a sufficient number of conversions from MCI to AD to complete a clinical trial (R.C. Petersen, 2003). Subjects with MCI are of particular interest since they represent a population at particularly high risk of converting to AD and a population in which secondary prevention trials can be carried out. In the case of normal subjects, conversion to AD is very slow, averaging only 1-2 % / year depending on the age of the cohort. Thus, primary prevention trials for AD require 3,000-6,000 subjects followed for 5 to 7 years to achieve sufficient clinical endpoints. These long timeframes are necessary because there is a great deal of variability in clinical endpoints based on slope analysis. For example, the standard deviation of the rate of change for the ADAS-Cog, the most widely used cognitive measure in AD trials, is 1 to 1.5 times the one year rate of change. In subjects with MCI or AD, volumetric images of the whole brain, hippocampus or entorhinal cortex have a smaller ratio of standard deviation of change to the rate of atrophy than clinical measures allowing for detection of a smaller effect size (Grundman et al., 2002). Thus, the development of suitable biomarkers that track the progression of the disease and reflect change in underlying pathology has two advantages over clinical/cognitive data: 1) They have greater statistical power to detect treatment effects, because of reduced measurement error and 2) They provide measurements which may directly (e.g., changes in brain size, measurements of CSF proteins) indicate rate of disease progression. Therefore, the use of such biomarkers could markedly speed drug development by providing an earlier signal of drug efficacy.