Biomarker‐based definition of limbic‐predominant long‐lasting amnestic mild cognitive impairment: Neuroimaging / Optimal neuroimaging measures for early detection. (7th December 2020)

• Record Type: Journal Article
• Authors: Tondo, Giacomo; Carli, Giulia; Santangelo, Roberto; Mattoli, Maria V; Presotto, Luca; Filippi, Massimo; Magnani, Giuseppe; Iannaccone, Sandro; Cerami, Chiara; Perani, Daniela
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.037879
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Previous studies described amnestic Mild Cognitive Impairment (aMCI) subjects with slow rate of cognitive decline, benign disease course associated to FDG‐PET brain hypometabolism in the medial temporal lobe structures. Clinical and post‐mortem studies suggested the presence of both Alzheimer's disease (AD) and non‐AD pathology. The identification of aMCI with a benign course has relevant consequences both for prognosis and treatment. The aim of this study was to define the role of in vivo biomarkers to predict prognosis in a large population of aMCI with long‐lasting disease course. Method: We selected 80 aMCI ( Cohort1 ) using the following criteria: observational time of disease duration ≥4 years; available baseline and follow‐up clinical and neuropsychological evaluations; baseline CSF analysis measuring amyloid‐β42, total‐tau and phosphorylated‐tau; FDG‐PET assessed for individual brain hypometabolism by a validated SPM procedure on a voxel‐by‐voxel basis, showing a selective medial temporal involvement, thus a non‐AD brain pattern. In addition, we selected 42 aMCI due to AD with similar baseline clinical features and biomarker measurements, and both CSF positive for amyloidopathy and FDG‐PET showing the typical AD temporo‐parietal brain hypometabolism ( Cohort2 ). Result: Cohort1 had mean disease duration of 8.45±3.37 years, range 4‐19 years, no decline in MMSE in most subjects, only 7% conversion to AD dementia at the clinical follow‐up. In Cohort2, 81% of aMCI converted to AD. The FDG‐PET single subject analysis predicted progression in Cohort2, or stability in Cohort1 with high accuracy (AUC=0.88), sensitivity (0.85) and specificity (0.90). Within Cohort1, the CSF biomarkers showed great variability; none of the CSF biomarkers was able to predict stability or conversion. According to the AT(N) classification, in Cohort1, 54% of subjects showed an AD profile and 46% a non‐AD profile. These two groups did not show any difference in cognitive and neuropsychological features at follow‐up, showing comparable stability. Conclusion: The specific brain hypometabolism pattern in Cohort1 is associated with clinical stability and slow rate of memory deficit progression. Nor CSF biomarkers, neither the AT(N) classification, can predict prognosis in this population. These findings underline the key role of FDG‐PET brain metabolism as a fundamental biomarker in the diagnostic and prognostic challenge of aMCI.