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P2‐234: APOE E4 Genotype Influence on Cerebral Metabolism in Mild Cognitive Impairment: Amyloid Burden–Adjusted Analysis
Author(s) -
Seo Eun Hyun,
Kim Sang Hoon,
Park Sang Hak,
Kang Seong-Ho,
Choo Il Han
Publication year - 2016
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2016.06.1402
Subject(s) - apolipoprotein e , precuneus , medicine , standardized uptake value , positron emission tomography , neuroimaging , alzheimer's disease , hippocampus , alzheimer's disease neuroimaging initiative , fluorodeoxyglucose , psychology , cognitive decline , cardiology , oncology , cognition , neuroscience , nuclear medicine , dementia , disease
be seen in Table. The best predictive accuracy achieved by the NC amyloidosis classifier was 79% (AUC1⁄40.83) using only APOE4, age, and gender. The best predictive accuracy achieved by the MCI amyloidosis classifier was 82% (AUC1⁄40.81) using APOE4, FERMT2, ABCA7, SORL1, and EPHA1. The best predictive accuracy achieved by the AD amyloidosis classifier was 90% (AUC1⁄40.77) using APOE4, gender, DSG2, MEF2C, EPHA1, age, and BIN1. Conclusions:Automated multimodal classifiers using AD risk genes show a promise for predicting brain amyloidosis. Further improvement of classifier accuracy may be achieved by the addition of other cognitive or biomarker measures.