Fox, Nick C.

AbstractBackgroundBlood phosphorylated tau (p‐tau) has proven to be the primary candidate as an accessible and scalable biomarker for Alzheimer’s disease (AD). Both p‐tau181 and p‐tau217 are highly accurate to detect AD within dementia cases. Our recent work in cerebrospinal fluid, however, indicates p‐tau231 to be associated to incipient AD pathology. In light of this, we developed a Single molecule array (Simoa) to detect plasma p‐tau231 and assessed its performance in multiple domains, specifically, its ability to detect preclinical AD.MethodIn all cohorts, plasma p‐tau231 was quantified using an in‐house Simoa method developed at the Clinical Neurochemistry Laboratory, Gothenburg University, Sweden. Data presented in this study are samples acquired from Translational Biomarkers of Aging and Dementia (TRIAD, n=503), King’s College London (n=55), Mayo Clinic Study of Aging (n=145), KARVIAH (n=140), Baltimore Longitudinal Study of Aging (n=430), University College London (n=70), Paris University (n=213), PREVENT‐AD (n=257), University of California (n=309) and ALFA+ (n=385).ResultIn the TRIAD cohort, plasma p‐tau231 demonstrated high accuracy in determining AD from young individuals (AUC=0.95), Aβ‐ elderly (AUC=0.92), Aβ‐ MCI (AUC=0.88) and other neurodegenerative disorders (AUC=0.92). In an autopsy study, plasma p‐tau231 could detect neuropathologically confirmed AD amongst dementia cases (AUC=0.99). In comparing healthy elderly Aβ‐ and Aβ+ individuals, p‐tau231 predicted preclinical Aβ pathology (AUC=0.83). Furthermore, inflection points of the weighted regression curves demonstrate that increases in plasma p‐tau231 occur before plasma p‐tau181 as a function of Aβ deposition load. Plasma p‐tau231 data from additional cohorts will focus on cross‐sectional and longitudinal preclinical datasets. In addition, further data will show the performance of p‐tau231 in symptomatic and presymptomatic PSEN1/APP mutation carriers, memory clinic cohorts, longitudinal neuropathology confirmed and acute neurological injury.ConclusionPlasma p‐tau231 is a new biomarker specific for AD pathology and at higher concentrations relative to p‐tau181 and p‐tau217. Our initial results indicate that plasma p‐tau231 is equivalent to p‐tau181 in diagnostic utility but has greater potential as a biomarker to monitor emerging Aβ pathology. This presentation will give an overview of novel cross‐sectional and longitudinal plasma p‐tau231 data, in more than 3000 individuals, that encompasses the AD continuum, familial AD and other neurological conditions.

AbstractAs prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and “sporadic” late‐onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy‐confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy‐confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences.

Abstract Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer’s disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain’s connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer’s disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer’s disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer’s disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer’s disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer’s sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer’s disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer’s disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer’s disease.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

AbstractNeuronal intranuclear inclusion disease (NIID) is a clinically heterogeneous neurodegenerative condition characterized by pathological intranuclear eosinophilic inclusions. A CGG repeat expansion in NOTCH2NLC was recently identified to be associated with NIID in patients of Japanese descent. We screened pathologically confirmed European NIID, cases of neurodegenerative disease with intranuclear inclusions and applied in silico‐based screening using whole‐genome sequencing data from 20 536 participants in the 100 000 Genomes Project. We identified a single European case harbouring the pathogenic repeat expansion with a distinct haplotype structure. Thus, we propose new diagnostic criteria as European NIID represents a distinct disease entity from East Asian cases.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

AbstractIntroductionFour less well‐studied but promising “emerging” cerebrospinal fluid (CSF) biomarkers are elevated in late‐onset Alzheimer disease (AD): neurogranin, synaptosomal‐associated protein‐25 (SNAP‐25), visinin‐like protein 1 (VILIP‐1), and chitinase‐3‐like protein 1 (YKL‐40).MethodsCSF neurogranin, SNAP‐25, VILIP‐1, and YKL‐40 were measured in families carrying autosomal‐dominant AD mutations.ResultsThe four emerging CSF biomarkers were significantly elevated in the mutation carriers (n = 235) versus noncarriers (n = 145). CSF SNAP‐25, VILIP‐1, and YKL‐40 were altered very early in the AD time course, approximately 15–19 years before estimated symptom onset. All CSF biomarkers predicted important AD‐related outcomes including performance on a cognitive composite, brain amyloid burden as measured by amyloid positron emission tomography, and the estimated years from symptom onset.DiscussionEarly abnormalities in CSF tTau, pTau, SNAP‐25, VILIP‐1, and YKL‐40 suggest that synaptic damage, neuronal injury, and neuroinflammation begin shortly after the commencement of brain amyloid accumulation.

Aβ deposition in the basal ganglia is common in autosomal dominant Alzheimer’s disease. Vöglein et al. report an increased severity of motor symptoms in autosomal dominant versus sporadic disease in advanced disease stages. Motor symptoms are more severe in post-codon 200 presenilin 1 mutation carriers and correlate with basal ganglia Aβ.