Prediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers
- Authors
- Jang, Hyemin; Park, Jongyun; Woo, Sookyoung; Kim, Seonwoo; Kim, Hee Jin; Na, Duk L.; Lockhart, Samuel N.; Kim, Yeshin; Kim, Ko Woon; Cho, Soo Hyun; Kim, Seung Joo; Seong, Joon-Kyung; Seo, Sang Won
- Issue Date
- 2019
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Amyloid; Mild cognitive impairment; Alzheimer' s disease; Multimodal biomarkers; Nomogram; Conversion to dementia
- Citation
- NEUROIMAGE-CLINICAL, v.24
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEUROIMAGE-CLINICAL
- Volume
- 24
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/68894
- DOI
- 10.1016/j.nicl.2019.101941
- ISSN
- 2213-1582
- Abstract
- It may be possible to classify patients with A beta positive (+) mild cognitive impairment (MCI) into fast and slow decliners according to their biomarker status. In this study, we aimed to develop a risk prediction model to predict fast decline in the A beta + MCI population using multimodal biomarkers. We included 186 A beta + MCI patients who underwent florbetapir PET, brain MRI, cerebrospinal fluid (CSF) analyses, and FDG PET at baseline. We defined conversion to dementia within 3 years (= fast decline) as the outcome. The associations of potential covariates (MCI stage, APOE4 genotype, corrected hippocampal volume (HV), FDG PET SUVR, AV45 PET SUVR, CSF A beta, total tau (t-tau), and phosphorylated tau (p-tau)) with the outcome were tested and nomograms were constructed using logistic regression models in the training dataset (n=124, n of fast decliners = 52). The model was internally validated with the testing dataset (n= 62, n of fast decliners = 22). The multivariable analysis (including CSF t-tau) showed that MCI stage (late MCI vs. early MCI; OR 15.88, 95% CI 4.59, 54.88), APOE4 (OR 5.65, 95% CI 1.52, 20.98), corrected HV*1000 (OR 0.22, 95% CI 0.09, 0.57), FDG SUVR*10 (OR 0.43, 95% CI 0.27, 0.71), and log(e) CSF t-tau (OR 6.20, 95% CI 1.48, 25.96) were associated with being fast decliners. In the second model including CSF p-tau instead of t-tau, the above associations remained the same, with a significant association between log(e) CSF p-tau (OR 4.53, 95% CI 1.26, 16.31) and fast decline. The constructed nomograms showed excellent predictive performance (90%) on validation with the testing dataset. Among A beta + MCI patients, our findings suggested that multimodal AD biomarkers are significantly associated with being classified as fast decliners. A nomogram incorporating these biomarkers might be useful in early treatment decisions or stratified enrollment of this population into clinical trials.
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