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Amyloid-beta is thought to facilitate the spread of tau throughout the neocortex in Alzheimer’s disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-beta, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during aging. There is evidence that in some cases amyloid-beta-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-beta. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer’s-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET based measures from two large observational studies: the Alzheimer’s Disease Neuroimaging Initiative and the Religious Orders Study and Rush Memory and Aging Project. We consistently identified ‘amyloid-first’ and ‘tau-first’ subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-beta precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-beta. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-beta accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer’s disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-beta-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of Primary Age-related Tauopathy. The rate of longitudinal amyloid-beta and tau accumulation (both measured via PET) within this group did not differ from normal aging, supporting the distinction of Primary Age-related Tauopathy from Alzheimer’s disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-beta and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-beta and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-beta and tau may inform research and clinical trials that target these pathologies.
Estimating the temporal evolution of biomarker abnormalities in disease informs understanding of early disease processes and facilitates subject staging, which may augment the development of early therapeutic interventions and provide personalised treatment tools. Event-based modelling of disease progression (EBM) is a data-driven technique for inferring a sequence of biomarker abnormalities, or events, from cross-sectional or short-term longitudinal datasets and has been applied to a variety of different diseases, including Alzheimer's disease (AD). Conventional EBM (C-EBM) assumes the sequence of biomarker abnormalities occurs in series, with one biomarker event per disease progression stage. However, events may occur simultaneously, for example due to the presence of shared causal factors, a property which cannot be inferred from C-EBM. Here we introduce simultaneous EBM (S-EBM), a generalisation of C-EBM to enable estimation of simultaneous events. S-EBM can estimate a wider range of sequence types than C-EBM while being fully backward compatible with the original model. Using simulated data, we firstly demonstrate the inability of C-EBM to infer simultaneous events. We next assess the accuracy of S-EBM against ground truth data and subsequently demonstrate a real-world example application to sequence disease progression in AD. Simulations show that C-EBM can not discern serial events with high biomarker variance from simultaneous events, preventing its use for inferring simultaneous events. S-EBM has high estimation accuracy against ground truth for a range of sequence types (fully simultaneous, partially simultaneous, serial), number of biomarkers and biomarker variances. When applied to Alzheimer's disease biomarker data from ADNI, S-EBM estimated a sequence where events within sets of biomarker domains occur simultaneously. Accumulation of total and phosphorylated tau in cerebrospinal fluid; performance on RAVLT, ADAS-Cog and MMSE cognitive test scores; and volumetric decline in temporal regional brain volumes, were better described as groups of simultaneous events rather than a single set of serial events (likelihood ratio >> 1,000). Furthermore, C-EBM may be confidently incorrect regarding the serial ordering. S-EBM may be applied to prospective and retrospective biomarker data to refine understanding of disease progression and generate new hypotheses regarding disease aetiology and spread.
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