Whitwell, Jennifer L.

Abstract Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer’s clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer’s disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion.

Abstract Alzheimer’s disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer’s disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer’s disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer’s disease (18 with typical amnestic Alzheimer’s disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer’s disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer’s disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer’s disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer’s disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer’s disease.

AbstractObjectiveTo assess the relationships between MRI volumetry and [18F]flortaucipir PET of typical and atypical clinical phenotypes of Alzheimer’s disease, by genarian (age by decade).MethodsFive‐hundred and sixty‐four participants including those with typical (n = 86) or atypical (n = 80) Alzheimer’s dementia and normal controls (n = 398) underwent apolipoprotein E genotyping, MRI, flortaucipir, and 11C‐PiB; all 166 Alzheimer’s participants were beta‐amyloid positive and all controls were beta‐amyloid negative. Grey matter volume and flortaucipir standard uptake value ratios were calculated for hippocampus, entorhinal cortex, and neocortex. Ratios of hippocampal‐to‐neocortical and entorhinal‐to‐neocortical volume and flortaucipir uptake were also calculated. Linear regression models assessed relationships among regional volume, flortaucipir uptake, and ratios and phenotypes, within three genarians (50–59, 60–69, and 70+). Voxel‐level analyses were also performed.ResultsFor 50–59 greater medial temporal atrophy and flortaucipir uptake was observed in the typical compared with atypical phenotype. The typical phenotype also showed greater frontal neocortex uptake with the voxel‐level analysis. For 60–69 and 70+ there was greater hippocampal volume loss in the typical compared with atypical phenotype while only the 60–69, but not the 70+ group, showed a difference in hippocampal flortaucipir uptake. We also observed a pattern for higher neocortical flortaucipir uptake to correlate with younger age decade for both phenotypes.InterpretationMRI volumetry versus flortaucipir PET relationships differ across Alzheimer’s clinical phenotypes, and also within phenotype across age decades. This suggests that there is potential risk of masked effects by not accounting for genarian in participants with beta‐amyloid and tau‐positive biomarker defined Alzheimer’s disease.

Background: Transactive response DNA-binding protein of 43 kDa (TDP-43) is associated with hippocampal atrophy in Alzheimer’s disease (AD), but whether the association is modified by other factors is unknown. Objective: To evaluate whether the associations between TDP-43 and hippocampal volume and atrophy rate are affected by age, gender, apolipoprotein E ( APOE) ɛ4, Lewy bodies (LBs), amyloid-β (Aβ), or Braak neurofibrillary tangle (NFT) stage. Methods: In this longitudinal neuroimaging-clinicopathological study of 468 cases with AD neuropathological changes (Aβ-positive) that had completed antemortem head MRI, we investigated how age, gender, APOE ɛ4, presence of LBs, Aβ, TDP-43, and Braak NFT stages are associated with hippocampal volumes and rates of atrophy over time. We included field strength in the models since our cohort included 1.5T and 3T scans. We then determined whether the associations between hippocampal atrophy and TDP-43 are modified by these factors using mixed effects models. Results: Older age, female gender, APOE ɛ4, higher field strength, higher TDP-43, and Braak NFT stages were associated with smaller hippocampi. Rate of atrophy was greater with higher TDP-43 and Braak NFT stage, but lower in older patients. The association of TDP-43 with greater rate of atrophy was enhanced in APOE ɛ4 carriers ( p = 0.04). Conclusion: Neurodegenerative effects of TDP-43 seem to be independent of most factors except perhaps APOE in cases with AD neuropathological changes. TDP-43 and tau appear to behave independently of one another.

Purpose The primary aim was to examine the utility of the Western Aphasia Battery–Revised (WAB-R; Kertesz, 2007 ) for classifying variants of primary progressive aphasia (PPA). Traditional WAB-R metrics of Aphasia Quotient (AQ), subtest scores, WAB-R classification, and several novel metrics were examined. A secondary aim was to examine these same WAB-R metrics in individuals with primary progressive apraxia of speech (PPAOS). Method A retrospective analysis of WAB-R records from 169 participants enrolled in a study of neurodegenerative speech and language disorders was conducted. PPA/PPAOS classification was determined by consensus review of speech, language, and cognitive profiles. Scores on each of the WAB-R subtests were obtained to derive AQ, WAB-R aphasia profile, and 3 ratios reflecting relative performance on subtests. Results Mean AQ was significantly higher in the PPAOS group compared to all PPA variants except primary fluent aphasia. AQ above the normal cutoff was observed for 20% of participants with PPA. Significant main effects of group were noted for each of the subtests. Follow-up comparisons most frequently discriminated PPAOS, primary agrammatic aphasia (PAA), and logopenic progressive aphasia. Primary fluent aphasia and semantic dementia (SD) subtest scores were less distinctive, with the exception of Naming for SD, which was significantly lower than for PAA and PPAOS. When the WAB-R AQ detected aphasia, a classification of anomic aphasia was most frequently observed; this pattern held true for each of the PPA variants. The mean Information Content:Naming ratio was highest for SD, and the mean Comprehension:Fluency ratio was highest for PAA. Conclusions In the current study, AQ underestimated the presence of PPA and WAB-R classification did not distinguish among PPA classification determined by consensus. Performance on individual subtests and relative performance across subtests demonstrated inconsistent alignment with PPA classification. We conclude the WAB-R in isolation is inadequate to detect or characterize PPA. We instead suggest utilizing the WAB-R as 1 component of a comprehensive language and motor speech assessment when PPA is suspected.