Prediction of Cerebral Amyloid With Common Information Obtained From Memory Clinic Practice - Lee, Jun Ho; Byun, Min Soo; Yi, Dahyun; Sohn, Bo Kyung; Jeon, So Yeon; Lee, Younghwa; Lee, Jun-Young; Kim, Yu Kyeong; Lee, Yun-Sang; Lee, Dong Young (Frontiers in Aging Neuroscience, 2018)
Background: Given the barriers prohibiting the broader utilization of amyloid imaging and high screening failure rate in clinical trials, an easily available and valid screening method for identifying cognitively impaired patients with cerebral amyloid deposition is needed. Therefore, we developed a prediction model for cerebral amyloid positivity in cognitively impaired patients using variables that are routinely obtained in memory clinics. Methods: Six hundred and fifty two cognitively impaired subjects from the Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer disease (KBASE) and the Alzheimer’s Disease Neuroimaging Initiative-2 (ADNI-2) cohorts were included in this study (107 amnestic mild cognitive impairment (MCI) and 69 Alzheimer’s disease (AD) dementia patients for KBASE cohort, and 332 MCI and 144 AD dementia patients for ADNI-2 cohort). Using the cross-sectional dataset from the KBASE cohort, a multivariate stepwise logistic regression analysis was conducted to develop a cerebral amyloid prediction model using variables commonly obtained in memory clinics. For each participant, the logit value derived from the final model was calculated, and the probability for being amyloid positive, which was calculated from the logit value, was named the amyloid prediction index. The final model was validated using an independent dataset from the ADNI-2 cohort. Results: The final model included age, sex, years of education, history of hypertension, apolipoprotein ε4 positivity, and score from a word list recall test. The model predicted that younger age, female sex, higher educational level, absence of hypertension history, presence of apolipoprotein ε4 allele, and lower score of word list recall test are associated with higher probability for being amyloid positive. The amyloid prediction index derived from the model was proven to be valid across the two cohorts. The area under the curve was 0.873 (95% confidence interval 0.815 to 0.918) for the KBASE cohort, and 0.808 (95% confidence interval = 0.769 to 0.842) for ADNI-2 cohort. Conclusion: The amyloid prediction index, which was based on commonly available clinical information, can be useful for screening cognitively impaired individuals with a high probability of amyloid deposition in therapeutic trials for early Alzheimer’s disease as well as in clinical practice.