FV3. Decomposing amnestic mild cognitive impairment by means of FDG-PET: Distinct hypometabolic subtypes differ in relation to biomarkers of Alzheimer's disease and clinical trajectories. Issue 8 (August 2018)

• Record Type: Journal Article
• Authors: Grothe, M.; Feitelson, J.; Dyrba, M.; Buchert, R.; Teipel, S.
• Is Part Of: Clinical neurophysiology. Volume 129:Issue 8(2018:Aug.)
• Page Start: e50
• Publication Date: 2018-08
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.617
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Background: Amnestic mild cognitive impairment (aMCI) is a clinical diagnosis with considerable etiologic heterogeneity. While the condition often represents prodromal Alzheimer's disease (AD), patients may also progress to other dementia types, stay stable, or even reverse to normal cognition. Detection of glucose hypometabolism by means of FDG-PET is a very sensitive in vivo marker of neuronal dysfunction, and represents one of the best established methods for early detection of AD-typical neurodegeneration. We aimed to characterize the heterogeneity of hypometabolic patterns among individuals diagnosed with aMCI. Methods: We applied Ward's hierarchical clustering to voxel-wise FDG-PET data of 329 aMCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to identify subgroups of distinct brain-wide metabolic patterns in this population. Identified subgroups were compared to 179 cognitively normal ADNI controls and characterized with respect to cognition, AV45-PET- and CSF-based biomarkers of amyloid and tau pathology, APOE genotype, as well as clinical progression over 2 years. Results: Four aMCI subgroups with markedly differing metabolic profiles were identified (Fig. 1 ): (i) an 'AD-typical' pattern of temporoparietal hypometabolism ( N = 135, 41%), (ii) restricted 'limbic' hypometabolism ( N = 56, 17%), (iii) a 'posterior ventral' hypometabolic pattern including the occipital lobe ( N = 56, 17%), and (iv) 'normal' metabolism that did not differ from control values of cognitively normal individuals ( N = 82, 25%). 'AD-typical' and 'limbic' subgroups exhibited biomarker characteristics consistent with AD, but differed in proportion of APOE4 carriers, tau levels, hippocampal atrophy, and cognitive trajectories. The 'posterior ventral' subgroup showed relatively mild amyloid uptake but no significant tau biomarker abnormalities, and aMCI patients with 'normal' metabolism did not show abnormal biomarkers nor cognitive decline. Conclusions: 'AD-typical' and 'limbic' hypometabolism likely represent distinct endophenotypes of prodromal AD, whereas a posterior ventral pattern of cortical hypometabolism may reflect non-AD pathologies, atypical AD variants, or comorbid conditions. One out of four individuals with clinically diagnosed aMCI did not show notable hypometabolic brain dysfunction, biomarker abnormalities, or clinical trajectories indicative of neurodegenerative disease.