See Lowe et al. (doi:) for a scientific commentary on this article.Alzheimer’s disease targets discrete neural systems, but the mechanisms underlying this regionally selective vulnerability remain unknown. Grothe et al. report that Alzheimer’s disease pathology specifically affects brain regions with certain molecular properties, and that the biological pathways underlying this vulnerability differ for amyloid accumulation versus neurodegeneration.
BackgroundCurrent methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer’s disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual’s amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC). We further examined its potential association with subtle cognitive impairments in this population at elevated risk for Alzheimer’s disease (AD).MethodsThe monocentric INSIGHT-preAD cohort includes 318 cognitively intact older individuals with SMC. All individuals underwent 18F-florbetapir PET scanning and extensive neuropsychological testing. We projected the regional amyloid uptake signal into the previously proposed hierarchical staging model of in vivo amyloid progression. We determined the adherence to this model across all cases and tested the association between increasing in vivo amyloid stage and cognitive performance using ANCOVA models.ResultsIn total, 156 participants (49%) showed evidence of regional amyloid deposition, and all but 2 of these (99%) adhered to the hierarchical regional pattern implied by the in vivo amyloid progression model. According to a conventional binary classification based on global signal (SUVRCereb = 1.10), individuals in stages III and IV were classified as amyloid-positive (except one in stage III), but 99% of individuals in stage I and even 28% of individuals in stage II were classified as amyloid-negative. Neither in vivo amyloid stage nor conventional binary amyloid status was significantly associated with cognitive performance in this preclinical cohort.ConclusionsThe proposed hierarchical staging scheme of PET-evidenced amyloid deposition generalizes well to data from an independent cohort of older people at elevated risk for AD. Future studies will determine the prognostic value of the staging approach for predicting longitudinal cognitive decline in older individuals at increased risk for AD.Electronic supplementary materialThe online version of this article (10.1186/s13195-019-0466-3) contains supplementary material, which is available to authorized users.
Highlights
Cognitively-defined subgroups among individuals with
AD dementia identified.
Subgroup gray matter volume patterns were distinct,
matching cognitive profiles.
Gene-set enrichment analyses revealed gray matter vs
gene expression associations.
These associations were partly shared between
subgroups, and partly unique.
Distinct biological drivers may be involved in
heterogeneity of Alzheimer’s disease.
Positron emission tomography (PET)‐based staging of regional amyloid deposition has recently emerged as a promising tool for sensitive detection and stratification of pathology progression in Alzheimer's Disease (AD). Here we present an updated methodological framework for PET‐based amyloid staging using region–specific amyloid‐positivity thresholds and assess its longitudinal validity using serial PET acquisitions. We defined region‐specific thresholds of amyloid‐positivity based on Florbetapir‐PET data of 13 young healthy individuals (age ≤ 45y), applied these thresholds to Florbetapir‐PET data of 179 cognitively normal older individuals to estimate a regional amyloid staging model, and tested this model in a larger sample of patients with mild cognitive impairment (N = 403) and AD dementia (N = 85). 2‐year follow‐up Florbetapir‐PET scans from a subset of this sample (N = 436) were used to assess the longitudinal validity of the cross‐sectional model based on individual stage transitions and data‐driven longitudinal trajectory modeling. Results show a remarkable congruence between cross‐sectionally estimated and longitudinally modeled trajectories of amyloid accumulation, beginning in anterior temporal areas, followed by frontal and medial parietal areas, the remaining associative neocortex, and finally primary sensory‐motor areas and subcortical regions. Over 98% of individual amyloid deposition profiles and longitudinal stage transitions adhered to this staging scheme of regional pathology progression, which was further supported by corresponding changes in cerebrospinal fluid biomarkers. In conclusion, we provide a methodological refinement and longitudinal validation of PET‐based staging of regional amyloid accumulation, which may help improving early detection and in‐vivo stratification of pathologic disease progression in AD.
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