期刊
NEUROIMAGE-CLINICAL
卷 24, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2019.101941
关键词
Amyloid; Mild cognitive impairment; Alzheimer's disease; Multimodal biomarkers; Nomogram; Conversion to dementia
类别
资金
- Brain Research Program through the National Research Foundation (NRF) of Korea [2016M3C7A1913844]
- Korea government (MSIP) through the NRF of Korea [2017R1A2B2005081]
- Brain Research Program of the National Research Foundation (NRF) - Ministry of Science ICT [NRF-2018M3C7A1056512]
- Research of Korea Centers for Disease Control and Prevention [2018-ER6203-01]
- Brain Research Program through the NRF - Ministry of Science ICT [2017M3C7A1048092]
- Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea [HI18C1629]
- Korea Health Promotion Institute [2018-ER6203-01] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2017R1A2B2005081, 2018M3C7A1056512] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据