Citations

AbstractBackgroundAdults with Down Syndrome (DS) develop Alzheimer disease (AD)‐like pathology and dementia as they age. This is attributed to triplication of the amyloid precursor protein gene. Recent work demonstrated that amyloid is elevated in DS and accumulates in a similar topography to autosomal dominant AD (ADAD). Tau accumulation is the second hallmark of AD and may be measured using tau positron emission tomography (PET) imaging. The pattern of tau accumulation remains unknown in DS.MethodIn a cross‐sectional analysis we evaluated amyloid and tau positron emission tomography (PET) in 124 participants with DS (109 cognitively stable, 8 symptomatic, 7 no consensus), 29 ADAD mutation carriers (26 asymptomatic, 3 symptomatic), and 201 cognitively normal non‐mutation carriers (CN). Regional cortical standard uptake value ratios (SUVR) for tau imaging were obtained and compared by cognitive status. Similarly, regional cortical amyloid SUVR were obtained and converted to centiloids. We then compared the magnitude of tau burden as well as spatial spread of tau across groups relative to amyloid burden.ResultCompared to CN, tau PET binding was elevated in participants with (µDS = 2.12 ± 0.22 SUVR, µADAD = 2.52 ± 0.37 SUVR) and without (µDS = 1.24 ± 0.15 SUVR, µADAD = 1.16 ± 0.19 SUVR) cognitive changes. For both groups, tau increased with increasing amyloid (centiloids). In some regions, DS had greater tau burden for a given amyloid level (centiloids) (Figure 1). There was increased spatial distribution of tau throughout the brain for participants with DS compared to CN. In contrast, tau burden was more focal in participants with ADAD with tau primarily elevated in posterior regions (Figure 2).ConclusionWhile tau burden was higher with symptomatic disease for both groups, significant differences existed in the overall amount and spatial spread. Tau spread was greatest in participants with DS and more diffuse compared to ADAD. These results indicate a substantial tau burden is present in DS, and suggest not only anti‐amyloid but also tau‐reducing agents should be considered for treating cognitive decline in adults with DS.

AbstractBackgroundThe Alzheimer’s Association and Society for Nuclear Medicine and Molecular Imaging convened a Workgroup to update previous Appropriate Use Criteria (AUC) for amyloid PET (Johnson 2013) and develop the first AUC for tau PET. The AUC represent general guidelines and should not be considered a substitute for clinical judgment exercised in the care of individual patients.MethodThe Workgroup included 14 researchers with multidisciplinary expertise. Meetings were held between June 2020 and August 2021. Key research questions were identified using the PICOTS framework, triggering an independent systematic literature review. Using established methods (Fitch 2001), the Workgroup identified 17 clinical scenarios in which amyloid/tau PET may be considered. Appropriateness of amyloid and tau PET as independent, stand‐alone modalities were ranked for each scenario using a 9‐point scale (Table 1). Consensus was achieved using a modified Delphi process. Online surveys were completed iteratively by Workgroup members, followed by discussion, until all votes for each scenario fell within one of the following categories: Appropriate/Uncertain/Rarely Appropriate.ResultAs an over‐arching principle, amyloid/tau PET should be considered in patients who: (1) have undergone a comprehensive assessment by a dementia expert; (2) Alzheimer’s disease (AD) is a diagnostic possibility but uncertainty remains; and (3) knowledge of amyloid/tau PET results is expected to help establish diagnosis and guide patient management. Consensus ratings for clinical scenarios are shown in Table 2. More evidence was available to inform recommendations for amyloid than tau PET. Amyloid PET was considered “Appropriate” to inform diagnosis of MCI or dementia due to suspected AD; clarify diagnosis when CSF biomarkers are equivocal; to inform prognosis in MCI; and to determine eligibility for amyloid‐targeting therapies. Tau PET was considered “Appropriate” to clarify diagnosis in patients with MCI/dementia under age 65 or those with atypical presentations; and to inform prognosis in MCI or dementia due to suspected AD.ConclusionThe updated AUC highlight a growing role for amyloid PET and an emerging role for tau PET in the clinical evaluation of cognitively impaired patients. AUC are expected to further evolve based on data from ongoing studies of clinical utility and a rapidly developing therapeutic landscape.

AbstractBackgroundDisease‐modifying therapies for Alzheimer’s Disease (AD) are likely most beneficial when initiated in the pre‐symptomatic phase. To track success of such interventions fluid biomarkers became instrumental, with neurofilament light chain (NfL) showing particular promise. We previously reported that serum NfL increases in pre‐symptomatic phases of familial (autosomal‐dominantly inherited) AD (fAD) and that within‐person rate‐of‐change in NfL is an earlier predictor of fAD symptom onset compared to cross‐sectional levels. The aim of the study at hand was to extend these initial findings in a larger cohort.MethodParticipants enrolled in the Dominantly Inherited Alzheimer Network (DIAN) with matched cross‐sectional and longitudinal cerebrospinal fluid (CSF; n = 962) as well as plasma (n = 1294) samples were used. NfL measurements have been performed on the SIMOA platform using commercially available assay‐kits. To investigate the influence of physiological factors independent from AD, we first analyzed whether age and body mass index (BMI) could account for observed variation in inter‐individual CSF and plasma NfL levels considering non‐carrier family‐members (NC) as a healthy control group. Next, utilizing estimated years to symptom onset (EYO) and previously published methods (Preische et al., 2019), we determined the point in disease course when baseline and longitudinal CSF and plasma NfL concentrations started to increase in mutation carriers (MC) relative to NC, after adjusting for age and BMI.ResultOur results reveal a tight correlation of CSF and blood NfL values within MC as well as NC (figure 1A‐B). However, within NC, after correction for age and BMI, some unexplained variability remained (figure 1C). Further, a decrease of the NfL plasma/CSF ratio over age was found after correcting for BMI (figure 1D). The discrimination of MC from NC was equally good for plasma compared to CSF, being possible on the group level as early as around ‐15 to almost ‐20 EYO depending on the modelling (figure 2).ConclusionOur results support plasma equivalently to CSF NfL as a clinically useful biomarker to longitudinally track neurodegeneration in fAD. The systemic factors influencing physiological NfL values in blood should be investigated in future studies to further improve interpretation of this biomarker.

AbstractBackgroundPlasma biomarkers of Alzheimer’s disease (AD) and neurodegeneration have demonstrated diagnostic utility and associations with AD pathology. Our objective was to map the average trajectories over age of plasma biomarkers and more established biomarkers including cognition, amyloid PET, and hippocampal volume.MethodWe included 1731 cognitively unimpaired (CU) and 226 mild cognitive impairment (MCI) participants from the Mayo Clinic Study of Aging (mean age of 70.3 years; 46.3% female; 14.8 years of mean education) with one or more measurements of amyloid‐PET, MRI with hippocampal volume measurements adjusted for head size (HVa), and plasma biomarkers (Aβ42/40 ratio, p‐tau181, GFAP, and NfL measured using Quanterix Simoa assays). A global cognitive z‐score was created by averaging the z‐scores of memory, language, executive function, and visuospatial domains. Smoothing spline curves were calculated for the values of each variable over age and placed on the same plot. Since each variable has a different scale, the smoothed curves were centered at the predicted value at age 50 and scaled so that a value of 1 would be one standard deviation of the fitted values from ages 50‐80.ResultAt age 50, GFAP and NfL start to increase, and Aβ42/40 was observed to decrease. At age 55, amyloid‐PET and p‐tau181 had a smooth and upward‐curving increase. After age 70, the average values of p‐tau181 increased at a higher rate. Average cognition started declining after 55 years. HVa began to decline after age 60 and then showed a steeper decline at age 72.ConclusionThis study demonstrates that predicted values of plasma GFAP, NfL, and Aβ42/40 across participants begin to change at younger ages than cognitive decline or increases observed in amyloid PET. The changes observed during middle age in GFAP and NfL likely reflect non‐AD pathologies and aging processes, while Aβ42/40 may be a leading indicator of future cerebral amyloidosis. Predicted plasma neurodegenerative biomarkers (GFAP and NfL) begin to change at younger ages than a traditional biomarker of neurodegeneration (HVa). Predicted ptau181 increases were observed in tandem with amyloid PET likely reflecting current amyloid status.

AbstractBackgroundKnowledge of the temporal evolution among plasma Alzheimer’s biomarkers and PET will inform disease pathophysiology and appropriate implementation of plasma biomarkers (Therneau, NeuroImage 2021; Betthauser, Brain 2022). This study investigates the relative timing of progression of plasma p‐tau181, p‐tau217, GFAP, and amyloid PET on individual and population levels.MethodWe included 957 participants from the Mayo Clinic Study of Aging with clinical diagnoses of cognitively unimpaired (n = 732), MCI (n = 205), and Alzheimer’s clinical syndrome dementia (n = 20). An accelerated failure time (AFT) model was fit with amyloid (PiB) PET, plasma p‐tau217 (Meso Scale Discovery, Lilly), plasma p‐tau181, and GFAP (Simoa, Quanterix HD‐X) as quad‐variate endpoints, and sex, APOE genotype status, and education as covariates. The model output included a per subject adjustment or time‐shift for each biomarker indicating how much earlier or later it was estimated to progress relative to the average participant, the correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing. Secondly, we estimated progression at a given age for selected cutpoints.ResultTemporal trajectories for each biomarker are shown in Figure 1. Individual‐level adjustments between plasma p‐tau217 and amyloid PET were highly correlated, R = 0.69(0.64‐0.74); how early or late an individual progressed on plasma p‐tau217 explained 48% of the variation in relative timing of amyloid PET progression. Associations of plasma p‐tau181 and GFAP with amyloid PET individual adjustments were moderate and low, respectively (Figure 2). Using the chosen cutpoints, the estimated order of biomarker progression was GFAP, amyloid PET, plasma p‐tau181, and plasma p‐tau217 (Figure 3).ConclusionOn the individual level, the strong association of the relative timing of progression of plasma p‐tau217 and amyloid PET supports that these processes are mechanistically related. The relatively lower associations of the timing of p‐tau181 and GFAP with amyloid PET likely reflect a lower biological association and greater measurement noise for p‐tau181. The individual adjustments, the primary output of this model, are independent of cutpoints. On the population level, relative timing of biomarker change is highly dependent on the cutpoints applied. This true for any analysis that employs cutpoints, regardless of the approach.

AbstractBackgroundPredictive models of tau‐PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer’s disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task‐free fMRI) and global organization (from FDG‐PET) to predict regional tau‐PET across AD phenotypes.MethodWe included 430 cognitively unimpaired (CU) individuals (214 A+T‐, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer’s Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as “Mayo cohort”). The connectivity approach consisted of assessing the relationship between a region’s tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG‐PET images that did not overlap with the Mayo cohort, 2) projecting FDG‐PET images of the Mayo cohort onto the low‐dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau‐PET in a linear model and determining optimal parameters based on predicted R 2 values, and 4) using the parameters from the model built in Step 3 to predict tau‐PET in the ADNI cohort (n = 86) (Fig.2).ResultThe connectivity‐based approach yielded adjusted R 2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R 2 and predicted R 2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R 2 in the ADNI cohort was 0.41 (range 0.10‐0.60) (Fig.3).ConclusionAn unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome‐based one to predict regional tau‐PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large‐scale physiology of the brain underlying specific mental functions rather than local connectivity factors.

AbstractBackgroundWhite matter (WM) plays an important role in cognitive function and undergoes substantial changes due to aging and increase in AD and AD related dementia (AD/ADRD) pathologies. While recent work proposed measurement of global WM changes from diffusion MRI as biomarkers for Cerebrovascular disease (CVD) such as PSMD and free water, there needs to be a better understanding of WM changes due to AD/ADRD pathologies. The goal of our recent work was to understand white matter changes with CVD and AD – common pathologies in AD/ADRD ‐ utilizing data from Mayo Clinic Study of Aging and Mayo ADRC.MethodUsing diffusion MRI (dMRI) in 718 participants from Mayo Clinic Study of Aging (mean age = 71.1 years, 56% male), we investigated relationships between commonly used dMRI measures and vascular risk as well as global amyloid PET. In a subset of 233 participants who were amyloid‐PET positive and also competed Tau‐PET (flortaucipir) from Mayo Clinic Study of Aging and Mayo ADRC, we investigated associations between regional flortaucipir SUVRs in Braak stages and regional WM changes.ResultsWe found greater frontal lobe WM damage with increasing vascular risk across all dMRI measures. We previously validated the strong relationship between the genu of the corpus callosum (anterior interhemispheric connections) dMRI measurements made on antemortem scans and cerebrovascular pathology observed on postmortem tissue (Nguyen et. al. Acta Neuropathologica 2022) supporting the utility of genu of the corpus callosum dMRI measures as a potential CVD biomarker. Also, we found greater posterior WM damage with increasing amyloid burden. Among the 233 amyloid positive participants, we found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages with greater extent of posterior WM damage. Further, dMRI measurements of the WM damage provided complementary information about disease staging and progression of AD in addition to flortaucipir SUVR measurements.ConclusionsAnterior WM measurements may be more specific to CVD and posterior WM changes are seen with increasing AD pathological burden. Our results suggest that WM changes observed with CVD and typical AD may be different and have clinical utility for differential diagnosis and disease staging.

AbstractBackgroundEEG slowing, a biomarker of cognitive decline in Alzheimer’s disease (AD), has been reported in wake or sleep EEG [1,2,3]. However, the relation between wake and sleep EEG and their comparative sensitivity to early stage of cognitive decline is less understood. In this study we compared sleep and wake EEG biomarkers using a common set of endpoints.MethodsParticipants included individuals with mild cognitive impairment (MCI, n = 30, ages 53‐88), Alzheimer’s disease dementia (ADem, n = 15, ages 60‐84), and controls with normal cognition (HC, n = 55, ages 50‐84). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). Sleep EEG recordings from AF7‐AF8 were collected in‐home and staged using Sleep ProfilerTM. Power spectral densities (PSD) and the derived Theta‐Alpha ratios were computed for wake EEG sites (counterpart bipolar montage), with the wake FP1‐FP2 compared by stage to the sleep EEG at AF7‐AF8. Significant differences in Theta‐Alpha ratios between MCI or ADem vs. HC groups were identified using independent t‐test (p<0.05) with the normalized effect size measured using Hedges‐g (E.S.).ResultsCorrelation coefficients between wake Alpha, Theta power and non‐REM sleep were: r = 0.83, 0.75 for stage N1, r = 0.71, 0.66 for stage N2, and r = 0.55, 0.50 for stage N3. Based on the frontal Theta‐Alpha ratios, the ADem group showed significant EEG slowing during both wake (E.S. = 0.73) and sleep (E.S. = 1.65, 1.31, 0.61, 1.71 for N1, N2, N3, and REM) as compared to the HC, while MCI group only exhibited EEG slowing during sleep (E.S. = 0.56, 0.77, 0.51 for N1, N2 and REM). Temporal (T5‐T6) wake EEG manifested significant slowing in both ADem (E.S. = 1.21) and MCI (E.S. = 0.56) groups. Compared to the HC, ADem patients exhibited significantly less sleep time (E.S. = 0.91, p = 0.002) and less stage REM (E.S. = 1.0, p = 0.001).ConclusionsCognitive decline in the AD spectrum is associated with common signatures in both sleep and wake EEG. However, the magnitude of these effects and their spatial distribution may be different in sleep versus wakefulness. These findings support a multi modal approach to neurophysiological assessment using both sleep and wakefulness data.

AbstractBackgroundThe causes of rapidly progressive dementia are heterogeneous, including neurodegenerative, vascular, and autoimmune/inflammatory diseases. Overlap in presenting symptoms and signs, results of cognitive testing, and findings on brain imaging and other common diagnostic tests confounds the etiologic diagnosis, contributing to diagnostic delays, missed opportunities for treatment, and poorer outcomes in patients with RPD. New tools and approaches are needed to improve diagnostic accuracy, with particular emphasis on improving early recognition of patients with potentially treatable causes of RPD.MethodsEstablished and emerging biomarkers of Alzheimer disease neuropathology (Aβ42/40, p‐tau181, p‐tau231), neuroaxonal and neuronal injury (neurofilament light [NfL], VILIP‐1, total tau), neuroinflammation (YKL‐40, sTREM2, GFAP, MCP‐1), and synaptic dysfunction (SNAP‐25, neurogranin) were measured in cerebrospinal fluid obtained at presentation from 78 prospectively accrued patients with RPD due to neurodegenerative (n = 35), vascular (n = 9), and autoimmune/inflammatory (n = 34) diseases, and 72 age‐ and sex‐similar cognitively normal individuals (controls). Causes of RPD were further classified as potentially treatment‐responsive or nonresponsive, referencing the extant literature. Biomarker levels were compared across etiologic diagnoses and by treatment responsiveness, referencing control values. Analyses were adjusted for the effects of age (ANCOVA).ResultsAlzheimer disease biomarkers (low Aβ42/40 and elevated p‐tau181 and p‐tau231) were associated with neurodegenerative causes of RPD (ANCOVA, p = 0.02 p = 0.30, p = 0.15), while high NfL (p = 0.61), low VILIP‐1 (p = 0.009), and elevated markers of inflammation (sTREM2, p = 0.009; YKL‐40, p<0.001) identified patients with autoimmune/inflammatory diseases (Figure). MCP‐1 levels were highest in patients with vascular causes of RPD (p = 0.08). 44 patients (56.4%) had potentially treatment‐responsive causes of RPD. Treatment responsiveness was associated with higher levels of CSF Aβ42/40 (p = 0.003), NfL (p<0.001), sTREM2 (p<0.001) and YKL‐40 (p<0.001); and lower levels of p‐tau‐181 and p‐tau231 (p<0.001, p<0.001) and VILIP‐1 (p<0.001).ConclusionSelected biomarkers measured in CSF at presentation associate with etiologic diagnosis and treatment responsiveness in patients with neurodegenerative, vascular, and autoimmune/inflammatory causes of RPD. Biomarker panels may be adapted to improve recognition of patients with treatment‐responsive causes of RPD early in the symptomatic course when treatments may be most effective.

AbstractBackgroundThe pathophysiological mechanisms driving frontotemporal lobar degeneration (FTLD) disease progression and corresponding biomarkers that capture these changes are not fully understood. We leveraged high‐throughput proteomics to identify networks of co‐expressed CSF proteins and candidate biomarkers for prediction of disease progression in adults with autosomal dominant FTLD mutations.Method113 adults carrying autosomal dominant FTLD mutations (C9orf72/GRN/MAPT) and 38 noncarrier controls from the ALLFTD consortium completed baseline lumbar puncture and longitudinal neuropsychological testing. CSF was assayed for SOMAmer proteomics on SOMAscan® v3.0 (4,140 proteins). Network analysis identified weighted protein co‐expression modules, which were annotated via gene ontology and cell‐type enrichment analysis. Disease age estimates that capture proximity to symptom onset were derived from a Bayesian disease progression model incorporating genotype‐specific clinical (cognition, CDR®+NACC FTLD) and biomarker (brain volume, plasma NfL) information. At baseline, local‐weighted regression examined module z‐scores (based on mean/SD of module levels [first principal component] in noncarriers) in relation to disease age. Longitudinal linear mixed‐effects models examined global cognitive trajectories in relation to baseline module z‐scores, as well as individual proteins within prognostic modules.ResultNetwork analysis identified 32 CSF protein co‐expression modules (37 to 223 proteins/module). 16 modules showed mutation‐related deviations from noncarriers. Deviations in ion transport, neurodevelopmental, metabolic, and transcriptional modules occurred several decades before estimated symptom onset (disease age: ‐30 to ‐20 years) and persisted across disease age. Deviations in neuroinflammatory and ubiquitination/autophagy modules occurred ∼10 years before estimated symptom onset. Sharp deviations in RNA binding, protein phosphorylation, neurogenesis, synaptic function, and lysosomal modules occurred near symptom onset (disease age: ‐1 to 4 years). Baseline synaptic and neurogenesis modules were the strongest prognosticators of cognitive decline (module x time: bs>.10, ps<.05). Within these modules, individual proteins with stronger intramodular connectivity exhibited stronger associations with cognitive decline (e.g., synaptic/transmembrane module: r = .40, p = 2.85e‐6; e.g., EPHA5, LRRTM2).ConclusionLarge‐scale analysis of the CSF proteome reveals a biologically‐diverse landscape of protein networks associated with presymptomatic and symptomatic stages of autosomal dominant FTLD. Synaptic and neuron‐enriched protein networks exhibited prodromal deviations that forecasted incipient cognitive decline. ‘Hub’ proteins derived from these affected networks warrant further validation as candidate biomarkers for early FTLD disease progression.