Measuring clinical progression in MCI and pre-MCI populations: enrichment and optimizing clinical outcomes over time

Suzanne B Hendrix

Recent biomarker research has improved the identification of individuals with very early stages of Alzheimer's disease (AD) and has demonstrated that biomarkers are sensitive for measuring progression in the pre-dementia or mild cognitive impairment (MCI) stage and even pre-symptomatic or pre-MCI stage of AD. Because there are no validated biomarkers in AD, it is important to seek out clinical outcomes that are also sensitive for measuring progression in these very early stages of disease. Clinical outcomes are more subjective and more affected by measurement error than biomarkers but represent the core aspects of the disease and are critical for validation of biomarkers and for evaluation of clinical relevance. Identification of individuals with pre-MCI stages of AD will need to continue to rely on biomarkers, but the identification of individuals with MCI who will progress to AD can be achieved with biomarkers or clinical criteria. Although standard clinical outcomes have been shown to be less sensitive to progression than biomarker outcomes in MCI and pre-MCI populations, non-standard scoring has improved the performance of the Alzheimer's Disease Assessment Scale cognitive subscale, making it more sensitive to progression. Neuropsychological cognitive testing items are optimal for measuring progression in pre-MCI populations, and current research is exploring the best ways to combine these items into a composite cognitive score with maximum responsiveness. In an MCI stage, cognitive, functional, and global items all change, and the best single composite score for measuring progression may involve all of these aspects of the disease. The best chance of success in demonstrating treatment effects in clinical trials will be achieved in a well-defined pre-MCI or MCI population and with an outcome that tracks well with clinical progression over time and with time. A partial least squares model can be used to identify these optimal weighted combinations.