Abstract
To date nearly all clinical trials of Alzheimer’s disease (AD) therapies have failed. These failures are, at least in part, attributable to poor endpoint choice and to inadequate recruitment criteria. Recently, focus has shifted to targeting at-risk populations in the preclinical stages of AD thus improved predictive markers for identifying individuals likely to progress to AD are crucial to help inform the sample of individuals to be recruited into clinical trials. We focus on hippocampal volume (HV) and assess the added benefit of combining HV and rate of hippocampal atrophy over time in relation to disease progression. Following the cross-validation of previously published estimates of the predictive value of HV, we consider a series of combinations of HV metrics and show that a combination of HV and rate of hippocampal atrophy characterises disease progression better than either measure individually. Furthermore, we demonstrate that the risk of disease progression associated with HV metrics does not differ significantly between clinical states. HV and rate of hippocampal atrophy should therefore be used in tandem when describing AD progression in at-risk individuals. Analyses also suggest that the effects of HV metrics are constant across the continuum of the early stages of the disease.
Highlights
As of 2012, phase III clinical trials of possible Alzheimer’s disease (AD) therapies had a failure rate of 99.6%1
A small number of studies[15,16] considered the association between rates of hippocampal atrophy and AD and found that greater hippocampal atrophy was associated with mild cognitive impairment (MCI) and AD diagnoses and that this association became stronger over time, albeit limited to 12 months from baseline
The aims of the study are three-fold: (1) to cross-validate previously reported predictive scores related to hippocampal volume (HV) markers, (2) to identify the best combination of HV metrics in terms of understanding disease progression over time and (3) to investigate if different HV metrics should be used to maximise the precision in the estimation of the likelihood of progressing from different clinical states (i.e. cognitively normal (CN) and MCI)
Summary
As of 2012, phase III clinical trials of possible Alzheimer’s disease (AD) therapies had a failure rate of 99.6%1. A number of factors are thought to have contributed to these failures, including sub-optimal patient-selection approaches (e.g. the targeting of individuals already diagnosed with AD as opposed to those in the preclinical stage of the disease), large variation both between and within individuals, poor endpoint choice, and potential measurement error in outcome and explanatory measures (biomarkers, imaging markers and neuropsychological assessments)[4,5] Another reason may be the inclusion of participants without evidence of amyloid pathology[6] thereby including individuals that are less likely to decline clinically over the duration of the trial. We bring together the findings of this study to discuss the manner in which the MRI methods described in this paper can be used in the patient screening phase of clinical trials of potential AD therapies to help increase power, as well as to inform the simulation of clinical trial design[4,17,18] prior to implementation
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