Gene‐ and age‐informed enrichment method for preclinical Alzheimer's disease trials: Approach and estimated cost savings: Biomarkers (non‐neuroimaging)/Use in clinical trial design and evaluation. (7th December 2020)

• Record Type: Journal Article
• Authors: Spencer, Barbara E.; Digma, Leonardino A.; Jennings, Robin G.; Brewer, James B.
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.040021
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Elevated β‐amyloid (Aβ) is used to enroll clinically normal (CN) individuals in preclinical Alzheimer's disease (AD) trials, but the screening process is inefficient and expensive. Novel enrichment methods are needed to improve efficiency of enrollment of these individuals while reducing costs. We hypothesized that by combining a polygenic hazard score (PHS) and age‐specific risk for AD we could predict elevated Aβ in CN individuals. Method: AD incidence rates and each participant's PHS were used to adjust the participant's chronological age based on protective or deleterious genetic factors, thus creating an ADAge. An individual's ADAge is defined as the chronological age at which there is equivalent AD risk in the population. Meng's test was used in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to assess whether the ADAge was more correlated with Aβ than was chronological age. An ADAge cutpoint was chosen to optimally predict Aβ positivity among 306 CN participants from the ADNI and applied to 1000 bootstrap samples of an independent validation cohort of 80 CN participants from the UC San Diego Shiley‐Marcos Alzheimer's Disease Research Center. The differences in means between samples enrolled by each strategy (ADAge enrichment vs no enrichment) were calculated to determine the efficiency of ADAge‐informed Aβ screening for preclinical AD trials as well as to determine the impact of ADAge enrichment on trial population demographics. The screening cost for enrolling 1000 Aβ+ CN participants in a clinical trial using each enrollment strategy was compared. Result: Compared to chronological age, ADAge was more correlated with Florbetapir SUVR in the ADNI cohort (p < .001). In the validation cohort, the ADAge‐enriched sample had a higher proportion of individuals with elevated Aβ (difference[95% CI] 0.19[0.07–0.33]) than the unenriched sample. The ADAge‐enriched sample was older than the unenriched sample (difference 3.00[0.94 – 5.22]). However, the samples were similar in the proportion of APOE ε4 carriers, the dementia rating scale score, and the proportion of female participants. ADAge enrichment lowered screening costs by $5.3 million (34%) in a clinical trial scenario. Conclusion: ADAge enrichment provides for a more efficient and cost‐effective means to enroll CN individuals with elevated Aβ in clinical trials.