With the advent of the first generation of disease-modifying treatments for Alzheimer's disease, it is clearer now more than ever that the field needs to move toward personalized medicine. Pooling data from past trials may help identify subgroups most likely to benefit from specific treatments and thus inform future trial design. In this perspective, we report on our effort to pool data from past Alzheimer's disease trials to identify patients most likely to respond to different treatments. We delineate challenges and hurdles, from our proof-of-principle study, for which we requested access to trial datasets from various pharmaceutical companies and encountered obstacles in the process of arranging data-sharing agreements through legal departments. Six phase I–III trials from three sponsors provided access to their data (total n = 3170), which included demographic information, vital signs, primary and secondary endpoints, and in a small subset, cerebrospinal fluid amyloid (n = 165, 5.2%) and tau (n = 212, 6.7%). Data could be analyzed only within specific data access platforms, limiting potential harmonization with data provided through other platforms. Limited overlap in terms of outcome measures, clinical and biological information hindered analyses. Thus, while it is a commendable advancement that (some) trials now allow researchers to study their data, we conclude that gaining access to past trial datasets is complicated, frustrating the field's communal effort to find the best treatments for the right individuals. We provide a plea to promote harmonization and open access to data, by urging trial sponsors and the academic research community alike to remove barriers to data access and improve collaboration through practicing open science and harmonizing outcome measures, to allow investigators to learn all there is to learn from past failures and successes.
Are reductions in the rate of decline from the new disease-modifying treatments (DMTs) in early Alzheimer's disease (AD) meaningful? We examined whether such reductions may be reflected in changes in health-related resource use.
Patients with Clinical Dementia Rating (CDR) = 0.5 or 1 with a clinical diagnosis of mild cognitive impairment or AD, reflecting clinical trial populations. Health-related resource use was reported using the Resource Use Inventory (RUI) including direct medical care, non-medical care, unpaid informal care, and time use.
Faster decline in CDR–Sum of Boxes (CDR-SB) from baseline was independently associated with higher likelihood and hours of informal care received, and lower likelihood of employment/volunteer work, but not with direct medical care.
Reductions in the rate of decline in CDR-SB seen from DMTs significantly affect patients’ work capacity and need for informal care, indicators of economic impact meaningful to patients, families, and health systems. These measures are not readily captured in administrative data sets.
Yulug B, Ayyildiz S, Sayman D, et al. The functional role of the pulvinar in discriminating between objective and subjective cognitive impairment in major depressive disorder. Alzheimer's Dement. 2024;10(1)e12450. doi:10.1002/trc2.12450
The second affiliation for Halil Aziz Velioglu was incorrectly listed as “School of Medicine, TUM-NIC Neuroimaging Center, Technical University of Munich, Munich, Germany”
The correct second affiliation is “Functional Imaging and Cognitive–Affective Neuroscience Lab (fINCAN), Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey”
We apologize for this error.
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