This study modeled predementia Alzheimer's disease clinical trials. Longitudinal data from cognitively normal (CN) and mild cognitive impairment (MCI) participants in the Alzheimer's Disease Neuroimaging Initiative were used to calculate sample size requirements for trials using outcome measures, including the Clinical Dementia Rating scale sum of boxes, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-cognitive subscale with and without delayed recall, and the Rey Auditory Verbal Learning Task. We examined the impact on sample sizes of enrichment for genetic and biomarker criteria, including cerebrospinal fluid protein and neuroimaging analyses. We observed little cognitive decline in the CN population at 36 months, regardless of the enrichment strategy. Nonetheless, in CN subjects, using Rey Auditory Verbal Learning Task total as an outcome at 36 months required the fewest subjects across enrichment strategies, with apolipoprotein E genotype ε4 carrier status requiring the fewest (n = 499 per arm to demonstrate a 25% reduction in disease progression). In MCI, enrichment reduced the required sample sizes for trials, relative to estimates based on all subjects. For MCI, the Clinical Dementia Rating scale sum of boxes consistently required the smallest sample sizes. We conclude that predementia clinical trial conduct in Alzheimer's disease is enhanced by the use of biomarker inclusion criteria.
"Using these biomarkers as outcome measures in trials would also have the potential to show a disease modifying effect on fewer subjects than standard cognitive tests, with proper enrichment strategies making these useful for predementia trials. (Grill et al., 2013; Schott et al., 2010). "
[Show abstract][Hide abstract] ABSTRACT: Brain atrophy measured using structural MRI has been widely used as an imaging biomarker for disease diagnosis and tracking of pathological progression in neurodegenerative diseases. In this work, we present a generalised and extended formulation of the Boundary Shift Integral (gBSI) using probabilistic segmentations in order to estimate anatomical changes between 2 time points. This method adaptively estimates a non-binary XOR region-of-interest from probabilistic brain segmentations of the baseline and repeat scans, in order to better localise and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for a hypothetical clinical trial of Alzheimer’s disease to that needed for the current implementation of BSI as well as a fuzzy implementation of BSI. The gBSI method results in a modest, but reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic XOR region.
"It seems unlikely that cerebrospinal fluid biomarkers, such as amyloid and tau concentrations, will reflect cognitive and behavioral deterioration better than neuropsychological assessment   . However, if one would combine neuropsychological measures as endpoints with sample enrichment strategies by neuroimaging, genetic, and neurochemical biomarkers, the statistical power of intervention studies might be increased even further   . "
[Show abstract][Hide abstract] ABSTRACT: Scales of global cognition and behavior, often used as endpoints for intervention trials in Alzheimer's disease (AD) and mild cognitive impairment (MCI), are insufficiently responsive (i.e., relatively insensitive to change). Large patient samples are needed to detect beneficial drug effects. Therefore, magnetic resonance imaging (MRI) measures of cerebral atrophy have been proposed as surrogate endpoints.
To examine how neuropsychological assessment compares to MRI in this respect.
We measured hippocampal atrophy, cortical thickness, and performance on neuropsychological tests in memory clinic patients at baseline and after two years. Neurologists rated the patients as cognitively normal (n = 28; Clinical Dementia Rating, CDR = 0) or as impaired (n = 34; CDR > 0). We administered five tests of memory, executive functioning, and verbal fluency. A composite neuropsychological score was calculated by taking the mean of the demographically corrected standard scores. MRI was done on a 3 Tesla scanner. Volumetric measurements of the hippocampus and surrounding cortex were made automatically using FreeSurfer software.
The composite neuropsychological score deteriorated 0.6 SD in the impaired group, and was virtually unchanged in the normal group. Annual hippocampal atrophy rates were 3.4% and 0.6% in the impaired and normal cognition groups, respectively. Estimates of required sample sizes to detect a 50% reduction in rate of change were larger using rate of hippocampal atrophy (n = 131) or cortical thickness (n = 488) as outcome compared to change scores on neuropsychological assessment (n = 62).
Neuropsychological assessment is more responsive than MRI measures of brain atrophy for detecting disease progression in memory clinic patients with MCI or AD.
[Show abstract][Hide abstract] ABSTRACT: The use of imaging biomarkers as patient inclusion criteria and as outcome markers is being considered to improve the efficacy of clinical trials in the early stages of Alzheimer’s disease (AD). As a secondary outcome marker, FDG PET can provide information about pharmacological effects within a few weeks in relatively small samples (typically 6–20 subjects per group), which is particularly useful in early phase 2 of drug development. In view of its ability to predict conversion to AD in patients with MCI, it could also be used for sample enrichment in phase 2/3 studies, but its relatively high cost compared to other indicators and current lack of regulatory approval for this purpose constitute considerable obstacles. Evidence from clinical trials and from observational longitudinal studies demonstrates that regional metabolic decline is closely linked to clinical progression, supporting the use of FDG PET in the assessment of disease-modifying interventions. Significant effects have been observed as early as 6 months after the start of interventions. Regional and statistical analysis of FDG PET data requires careful design and planning to minimize the risk of non-specific or false-positive results and to maximize information gain.
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