P2‐207: Combination of gray matter density and global cognitive information as a better predictor of the conversion to Alzheimer's disease in mild cognitive impairment: A two‐year follow‐up study

This study aimed to investigate Alzheimer' disease (AD) prediction ability of clinical assessment, neuropsychological tests, apolipoprotein E (ApoE) genotyping, [18 F] fluorodeoxyglucose positron emission tomography (FDG-PET), structural MRI, and diffusion tensor imaging (DTI) at baseline and to search the most effective model to predict AD in mild cognitive impairment (MCI) patients after a two-year follow-up period. Thirty two elderly subjects with MCI and 26 cognitively normal (CN) elderly individuals were evaluated at baseline with a sum of boxes score of clinical dementia rating (CDR-SOB), eight CERAD neuropsychological tests, the ApoE genotyping, FDG-PET, MRI, and DTI, and followed up annually for 2 years. Voxel-based statistical comparisons of baseline FDG-PET, MRI, DTI data were performed between AD converted MCI (MCIc) and non-converted MCI (MCInc). A series of logistic regression analyses were conducted to examine the AD prediction ability of each assessment modality alone and various combinations of modalities. Of MCI patients, 12 (37.5%) were converted to clinically evident AD (MCIc) and 20 (62.5%) were still in the MCI state (MCInc) after the 2-year follow-up. Compared with MCInc, MCIc showed reduced regional cerebral glucose metabolism (rCMglc) in the right inferior parietal lobule, right cingulate gyrus and left angular gyrus at baseline, reduced gray matter (GM) density in right inferior parietal lobule, lower CDR-SOB score, and lower mini-mental state examination (MMSE) score. The ApoE genotyping and CERAD neuropsychological tests except MMSE, and fractional anisotropy (FA) and mean diffusivity (MD) values derived from DTI were not significantly different between MCIc and MCInc. In terms of AD prediction after two-years in MCI, logistic regression analyses showed that the combination model including right inferior parietal lobule density on MRI and MMSE score (accuracy: 87.5%) was significantly better than any other models. The combination of GM density information on MRI and global cognitive score is probably the most cost-effective model to predict AD in MCI patients after a two-year follow-up period.