Graff‐Radford, Neill R.

AbstractBackgroundMost series exploring the causes of rapidly progressive dementia (RPD) rely on clinical diagnoses, with limited pathological confirmation. This approach may underestimate the contributions of Alzheimer disease and related dementias to RPD, recognizing challenges associated with extrapolating clinical phenotypes to neuropathological diagnoses. A clearer understanding of the neuropathological causes of RPD is needed to inform the diagnosis and treatment of patients. To meet this need, we determined the neuropathological causes of RPD within the Mayo Clinic neurodegenerative brain bank.MethodPatients with disease duration <4.0 years (symptom‐onset to death due to dementia) were identified within the Mayo Clinic neurodegenerative brain bank (1998‐2020). Available clinical records were reviewed, and details extracted including demographics, family history, dominant symptoms/signs at onset, and common comorbidities (depression, psychoses, sleep disturbance). Neuropathological diagnoses were assigned following standard protocols.Result310/8586 (3.6%) cases met RPD criteria. Relative to typically progressive cases, prion disease most commonly presented as RPD (74%, 32/43), followed by progressive supranuclear palsy/corticobasal degeneration (PSP/CBD: 7.5%, 142/1894), other frontotemporal lobar degeneration (FTLD: 5.7%, 32/561), Lewy body disease (LBD: 4.1%, 49/1202), and Alzheimer disease (AD: 1.8%, 48/2687). Average age‐at‐symptom onset was 69.5±10.4 years. Patients with rapidly progressive AD were older than others (p<0.01), except LBD. Patients with FTLD were younger than others (p<0.05), except prion disease. Average disease duration was 2.9±1.0 years: prion diseases had the most rapid disease course (1.6±1.3 years). Comorbid cerebrovascular disease (25.5%), and clinically symptomatic depression (41.3%), psychoses (37.1%) and sleep disturbances (39.4%) were common across groups. Only psychosis was associated with shorter disease duration (β=‐0.31 years, CI95% [‐0.53, ‐0.082]; controlling for age‐at‐symptom onset).ConclusionRapidly progressive neurodegenerative diseases accounted for 3.6% of cases in this neurodegenerative brain bank. Although prion disease commonly presented as RPD, atypical presentations of more prevalent neurodegenerative diseases accounted for most cases. Atypical (rapidly progressive) variants of typical neurodegenerative diseases warrant consideration in clinical practice.

AbstractBackgroundNeurofibrillary tangles are dynamic entities with a lifespan encompassing three maturity levels: intracellular pretangles, intracellular mature tangles, and extracellular ghost tangles. Proteins are suggested to be differentially expressed throughout the tangle lifespan; however, past studies may be limited by spatial expression or number of proteins investigated. We sought to assess differences in protein expression across the tangle lifespan using GeoMxTM Digital Spatial Profiler (DSP) by NanoString, a new technology allowing for spatially derived multiplex investigation.MethodWe used DSP to measure protein expression in hippocampi from Alzheimer’s disease (n=6) and nondemented controls (n=2) using the “Human Neural Cell Profiling Core” and “Alzheimer’s Pathology” module. Regions of interest (ROIs) were classified by the major tangle maturity level recognized by a primary fluorescent conjugated tau antibody. Protein expression was normalized to the geomean of the three housekeeping genes. The tau‐positive segment was specifically analyzed for comparisons. ANOVA was used for group‐wise comparisons and t‐test using the Benjamini‐Yekutieli adjustment for multiple tests was used for pair‐wise comparisons.ResultAs expected, cytoskeletal (e.g., MAP2, Tau) and neuronal (e.g., NeuN) markers generally decreased through the neurofibrillary tangle lifespan (Figure). Markers profiled as neurodegenerative (e.g., amyloid‐β1‐42, APOE, APP) generally increased through tangle maturity levels, except for TDP‐43 which decreased. Interestingly, there was no significant difference in ubiquitin expression. Astrocytic GFAP and S100B expression was significantly increased through each maturity level. Expression of microglial‐specific markers P2RY12 and TMEM119 was significantly elevated in ghost tangles compared to mature tangles. Additional microglial/macrophage markers including Iba‐1 and CD68 were generally increased as neurofibrillary tangles matured. Interestingly, APOE expression remained low in pretangles and mature tangles before dramatically increasing in ghost tangles (Figure).ConclusionOur findings suggest that proteins are differentially expressed throughout the tangle lifespan and the majority followed a monotonically directed pattern. Non‐monotonically directed protein patterns may reflect the death of the neuron during the ghost tangle maturity level. Future studies will test the hypothesis that the microenvironment of the hippocampus is altered with increasing vulnerability to tangle pathology.

AbstractBackgroundRates of cognitive and biomarker change in Autosomal Dominant Alzheimer disease (ADAD) vary substantially across individuals. Prior cross‐sectional work suggests that the location of the pathogenic variant within PSEN1, specifically whether the underlying variant affects transmembrane (TM) or cytoplasmic (CY) domains in PSEN1, may be a key determinant in these differential rates of progression. Here we use longitudinal data from the Dominantly Inherited Alzheimer Network observational study (DIAN‐Obs) to examine whether variants affecting TM versus CY domains in PSEN1 have differential rates of change in key cognitive and neurodegenerative markers, and whether these differences are relevant to ADAD clinical trials.MethodsUsing longitudinal clinical, cognitive, and MRI data from PSEN1 pathogenic variant carriers [TM group N=76 and CY group N=44; Table 1], we assessed rates of change in Mini‐Mental State Exam (MMSE), Clinical Dementia Rating® Sum of Boxes (CDR®‐SOB), and hippocampal volume (HV) using linear mixed effects models accounting for disease stage (estimated years to symptom onset [EYO]). We further assessed how PSEN1 mutation location (TM versus CY) impacts sample size and detectable effect size in a potential ADAD clinical trial (modeled as a 4‐year trial with annual assessments; 80% power; α = 0.05).ResultsPSEN1 TM and PSEN1 CY groups did not differ on baseline age, EYO, or CDR®. The PSEN1 TM group had significantly greater rates of change on MMSE (B[SE] = ‐0.42[0.1], p=0.002), CDR®‐SOB (B[SE] = 0.23[0.1], p=0.001), and HV atrophy (B[SE] = ‐58.93[14.3], p=0.0006 compared to the PSEN1 CY group (Fig.1). Consistent with these differential rates of change, power analyses indicated the required sample size to detect a 30% treatment effect on MMSE or HV would be reduced by 59.6% for MMSE and 91.0% for HV for a trial population comprised of PSEN1 TM versus CY carriers (Fig.2).ConclusionsIndividuals who had a variant affecting the transmembrane domains of PSEN1 had greater rates of cognitive decline and neurodegeneration compared to those with variants affecting cytoplasmic domains. In addition to having implications for ADAD pathophysiology, these results suggest that incorporating information regarding the location of PSEN1 variants may be beneficial in analyzing and designing stratification approaches for ADAD trials.

AbstractBackgroundWe recently discovered a novel tau binding partner, SERPINA5, to be upregulated in Alzheimer’s disease (AD) and co‐localize with corticolimbic tau distribution. As tau pathology is also observed in primary tauopathies, we hypothesized that SERPINA5 burden would mirror tau burden in these diseases. Thus, we sought to define the relationship between SERPINA5 and tau in primary tauopathies and compare this relationship to AD.MethodImmunohistochemistry was performed on the hippocampus or cingulate in 4 groups (n=6 per disease group): 1) AD, 2) 3R+4R primary age‐related tauopathy, 3) 4R tauopathies (progressive supranuclear palsy, corticobasal degeneration, argyrophilic grains disease), and 4) 3R tauopathy (Pick’s disease). Burden analyses for SERPINA5 and a phospho‐tau marker (AT8) were performed on digitized whole slide images.ResultVisual interpretation of SERPINA5 immunostaining identified both neuronal and glial inclusions that are commonly AT8‐positive in primary tauopathies. There were exceptions to this pattern such as SERPINA5‐positive Pick bodies being rarely observed. However, some pathologic structures, like ballooned neurons, were preferentially stained by SERPINA5. Quantitative analyses confirmed this observation with AT8 and SERPINA5 burden correlating in both the cingulate (R2=0.47, p<0.0001, Figure 1a) and hippocampus (R2=0.74, p<0.0001, Figure 1d). Nonparametric rank sum tests revealed majority of the primary tauopathies had less AT8 and SERPINA5 burden compared to AD in both the cingulate and hippocampus (Figure1b‐c, e‐f; Table 1).ConclusionSERPINA5 immunostaining patterns in primary tauopathies reflect tau immunostaining patterns. Our data suggests the SERPINA5‐tau interaction may not be specific for AD, which may implicate the role of SERPINA5 more broadly across tauopathies.

AbstractBackgroundRates of cognitive and biomarker change in Autosomal Dominant Alzheimer disease (ADAD) vary substantially across individuals. Prior cross‐sectional work suggests that the location of the pathogenic variant within PSEN1, specifically whether the underlying variant affects transmembrane (TM) or cytoplasmic (CY) domains in PSEN1, may be a key determinant in these differential rates of progression. Here we use longitudinal data from the Dominantly Inherited Alzheimer Network observational study (DIAN‐Obs) to examine whether variants affecting TM versus CY domains in PSEN1 have differential rates of change in key cognitive and neurodegenerative markers, and whether these differences are relevant to ADAD clinical trials.MethodsUsing longitudinal clinical, cognitive, and MRI data from PSEN1 pathogenic variant carriers [TM group N=76 and CY group N=44; Table 1], we assessed rates of change in Mini‐Mental State Exam (MMSE), Clinical Dementia Rating® Sum of Boxes (CDR®‐SOB), and hippocampal volume (HV) using linear mixed effects models accounting for disease stage (estimated years to symptom onset [EYO]). We further assessed how PSEN1 mutation location (TM versus CY) impacts sample size and detectable effect size in a potential ADAD clinical trial (modeled as a 4‐year trial with annual assessments; 80% power; α = 0.05).Results PSEN1 TM and PSEN1 CY groups did not differ on baseline age, EYO, or CDR®. The PSEN1 TM group had significantly greater rates of change on MMSE (B[SE] = ‐0.42[0.1], p=0.002), CDR®‐SOB (B[SE] = 0.23[0.1], p=0.001), and HV atrophy (B[SE] = ‐58.93[14.3], p=0.0006 compared to the PSEN1 CY group (Fig.1). Consistent with these differential rates of change, power analyses indicated the required sample size to detect a 30% treatment effect on MMSE or HV would be reduced by 59.6% for MMSE and 91.0% for HV for a trial population comprised of PSEN1 TM versus CY carriers (Fig.2).ConclusionsIndividuals who had a variant affecting the transmembrane domains of PSEN1 had greater rates of cognitive decline and neurodegeneration compared to those with variants affecting cytoplasmic domains. In addition to having implications for ADAD pathophysiology, these results suggest that incorporating information regarding the location of PSEN1 variants may be beneficial in analyzing and designing stratification approaches for ADAD trials.

AbstractBackgroundIn 2018 we presented the Computational DESH (CDESH) metric (DOI:10.1016/j.nicl.2018.11.015), a machine learning measure of abnormal CSF space patterns commonly observed on MRI in Disproportionately Enlarged Subarachnoid‐space Hydrocephalus (DESH‐type) NPH. We have shown that CDESH positive individuals often have discordant CSF and PET amyloid measures (DOI:10.1016/j.jalz.2019.06.4921). Positive CDESH scores are predictive of cognitive decline (DOI:10.1016/j.neuroimage.2021.117899, DOI: 10.1212/WNL.0000000000008616). The score is calculated from regional CSF volumes in a sulcal atlas and within the ventricles using a support vector machine (SVM). The Mayo Clinic Study of Aging (MCSA) changed scanner platforms (GE to Siemens) and T1‐weighted image resolution (1.2mm x 1.05mm x 1.05mm to 0.8mm isotropic) in 2017. We refer to these as “isotropic” or “anisotropic” scans. Our objective was to assess the dependence of a machine learning method (specifically CDESH) to image input variation and, if necessary, determine what needed corrections.MethodAll data considered is from the MCSA or Mayo Clinic ADRC. The SVM was trained on anisotropic images with laboriously labelled ground truth. We resampled the isotropic data from a crossover study (N=112, typically scanned same‐day with both platforms/resolutions) to match the anisotropic resolution. Two approaches to correction were evaluated (Figure 1). In Figure 2 we compare of the score with and without correction cross‐sectionally in 998 MCSA participants. In Figure 3 we present longitudinal series from 23 participants with multiple scans using the isotropic resolution protocol and at least one prior anisotropic image.ResultsComparisons of data from the crossover study are presented in Figure 1. Matching voxel resolution to the data used to train the SVM is critical and a subsequent mild linear score adjustment may be warranted. Cross‐sectional comparisons (Figure 2) confirm improvement matching voxel resolution with a possible mild linear score adjustment. After matching voxel resolution CDESH score trajectories (Figure 3) are markedly more stable across this scanner/protocol change.ConclusionThe SVM‐based CDESH score is sensitive to voxel resolution. Corrections are needed in image processing and possibly in the scalar output score to achieve stability across protocol and scanner changes. The resulting correction supports coherent long timeseries data and longitudinal extension of previous work.

AbstractBackgroundGiven the triplication of chromosome 21 and the location of the amyloid precursor protein gene on chromosome 21, almost all adults with Down syndrome (DS) develop Alzheimer disease (AD)‐like pathology and dementia during their lifetime. Comparing amyloid accumulation in DS to autosomal dominant AD (ADAD), another genetic form of AD, may improve our understanding of early AD pathology development.MethodWe assessed amyloid positron emission tomography (PET) imaging in 192 participants with DS and 33 sibling controls from the Alzheimer’s Biomarker Consortium‐Down Syndrome (ABC‐DS) and 265 mutation‐carriers (MC) and 169 familial controls from the Dominantly Inherited Alzheimer Network (DIAN) (Table 1). We calculated regional standard uptake value ratios (SUVR) using a cerebellar cortex reference region and converted global amyloid burden SUVR to centiloids. We compared amyloid PET by cognitive status and estimated‐years‐to‐symptom‐onset (EYO). EYO was calculated for DIAN participants by subtracting their age from parental age of symptom onset and for ABC‐DS participants by subtracting their age from 50.2 years, a published average age of symptom onset in a large sample of individuals with DS (Fortea et al., 2020). In a subset of participants, we assessed the relationship between amyloid PET and CSF Aβ42/40.ResultThe relationship between CSF Aβ42/40 and amyloid PET was similar in DS and MC participants (Figure 1). We did not observe significant differences between MC and DS grouped by cognitive status (Figure 2). However, when assessed over EYO, global amyloid burden was significantly elevated in MC at EYO ≥ ‐23 but was not elevated in DS until EYO ≥ ‐15 (Figure 3). We observed early cortical and subcortical amyloid PET increases in both groups, but we also measured some regional differences in amyloid PET changes between MC and DS, specifically in the medial occipital region (Figure 4 and 5).ConclusionThese results demonstrate similarities in the relationship between amyloid biomarkers and the levels of amyloid accumulation in ADAD and DS. However, we also observed a 5‐10 year delay and some regional differences in amyloid accumulation in DS. This is important for future clinical trials to consider when recruiting participants and determining treatment efficacy.

ImportancePhysical activity is associated with cognitive health, even in autosomal dominant forms of dementia. Higher physical activity is associated with slowed cognitive and functional declines over time in adults carrying autosomal dominant variants for frontotemporal lobar degeneration (FTLD), but whether axonal degeneration is a potential neuroprotective target of physical activity in individuals with FTLD is unknown.ObjectiveTo examine the association between physical activity and longitudinal neurofilament light chain (NfL) trajectories in individuals with autosomal dominant forms of FTLD.Design, Setting, and ParticipantsThis cohort study included individuals from the ALLFTD Consortium, which recruited patients from sites in the US and Canada. Symptomatic and asymptomatic adults with pathogenic variants in one of 3 common genes associated with FTLD (GRN, C9orf72, or MAPT) who reported baseline physical activity levels and completed annual blood draws were assessed annually for up to 4 years. Genotype, clinical measures, and blood draws were collected between December 2014 and June 2019; data were analyzed from August 2021 to January 2022. Associations between reported baseline physical activity and longitudinal plasma NfL changes were assessed using generalized linear mixed-effects models adjusting for baseline age, sex, education, functional severity, and motor symptoms.ExposuresBaseline physical activity levels reported via the Physical Activity Scale for the Elderly. To estimate effect sizes, marginal means were calculated at 3 levels of physical activity: 1 SD above the mean represented high physical activity, 0 SD represented average physical activity, and 1 SD below the mean represented low physical activity.Main Outcomes and MeasuresAnnual plasma NfL concentrations were measured with single-molecule array technology.ResultsOf 160 included FTLD variant carriers, 84 (52.5%) were female, and the mean (SD) age was 50.7 (14.7) years. A total of 51 (31.8%) were symptomatic, and 77 carried the C9orf72 variant; 39, GRN variant; and 44, MAPT variant. Higher baseline physical activity was associated with slower NfL trajectories over time. On average, NfL increased 45.8% (95% CI, 22.5 to 73.7) over 4 years in variant carriers. Variant carriers with high physical activity demonstrated 14.0% (95% CI, −22.7 to −4.3) slower NfL increases compared with those with average physical activity and 30% (95% CI, −52.2 to −8.8) slower NfL increases compared with those with low physical activity. Within genotype, C9orf72 and MAPT carriers with high physical activity evidenced 18% to 21% (95% CI, −43.4 to −7.2) attenuation in NfL, while the association between physical activity and NfL trajectory was not statistically significant in GRN carriers. Activities associated with higher cardiorespiratory and cognitive demands (sports, housework, and yardwork) were most strongly correlated with slower NfL trajectories (vs walking and strength training).Conclusions and RelevanceIn this study, higher reported physical activity was associated with slower progression of an axonal degeneration marker in individuals with autosomal dominant FTLD. Physical activity may serve as a primary prevention target in FTLD.