4.7 Article

Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment

期刊

JOURNAL OF CLINICAL MEDICINE
卷 11, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/jcm11123433

关键词

mild cognitive impairment; Alzheimer's disease; positron emission tomography; apolipoprotein E; medial temporal lobe atrophy

资金

  1. Ministry of Health & Welfare, Republic of Korea (Korea Health Technology R&D Project through the Korea Health Industry Development Institute) [HU20C0271, HU21C0016]
  2. Ministry of Science and ICT (the Original Technology Research Program for Brain Science through the National Research Foundation of Korea) [NRF-2018M3C7A1057137, NRF-2020M3E5D2A01084721]

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This study aimed to develop a new predictive scale for amyloid positron emission tomography (PET) positivity using easily accessible tools. The results showed that verbal and visual memory tests, medial temporal lobe atrophy, apolipoprotein E (APOE) genotype, and age were significant predictors of A beta deposition. The new predictive scale performed well in the validation cohort.
The presence of amyloid-beta (A beta) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer's disease dementia. Amyloid positron emission tomography (PET) has been used for detecting A beta deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the A beta-positive and A beta-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score >= 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting A beta positivity in aMCI patients in clinical practice.

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