Citations

AbstractBackgroundThough pathogenic variations in PSEN1 leading to Autosomal Dominant Alzheimer's disease (ADAD) are all highly penetrant, there is substantial inter‐individual variability in the rates of cognitive decline and biomarker change observed in ADAD. We hypothesized that this inter‐individual variability may be associated with the location of the pathogenic variant within PSEN1. We tested this hypothesis using biomarker and cognitive data from the Dominantly Inherited Alzheimer Network (DIAN) observational study.MethodPSEN1 pathogenic variant carriers [grouped based on whether their ADAD genotype affected transmembrane (PSEN1 TM; N = 125) or cytoplasmic (PSEN1 CY; N = 62) domains] and non‐carriers [NC; N = 176] enrolled in DIAN underwent clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF). Linear mixed effects models with a restricted cubic spline were used to determine cross‐sectional differences in clinical and biomarker measures between NC, PSEN1 TM, and PSEN1 CY groups, across the disease course (estimated years to symptom onset; EYO).ResultBackground characteristics are reported in Table 1. PSEN1 CY and PSEN1 TM groups had elevated β‐amyloid (PiB‐PET) and phosphorylated‐tau levels (CSF pTauT181) compared to NC, but these biomarker measures were not different between PSEN1 TM and CY groups (Figure 1a‐b). Additionally, the PSEN1 TM group had decreased glucose metabolism compared to NC but was similar to the PSEN1 CY group (Figure 1c). However, we observed that PSEN1 TM carriers had significantly smaller hippocampal volume, greater lateral ventricle volume, and greater cognitive impairment (Figure 1d‐f) across EYO as compared to PSEN1 CY carriers.ConclusionDespite similar β‐amyloid PET and CSF pTauT181 levels across EYO, individuals with pathogenic variants affecting transmembrane domains in PSEN1 may have more rapid progression of disease as measured by cognitive function and neurodegeneration as compared to those with variants affecting cytoplasmic domains. These results have implications both for understanding the heterogeneity in ongoing ADAD clinical trials and for better understanding the pathobiology of ADAD.

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.

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.

Abstract INTRODUCTION A biphasic model for brain structural changes in preclinical Alzheimer's disease (AD) could reconcile some conflicting and paradoxical findings in observational studies and anti‐amyloid clinical trials. METHODS In this study we tested this model fitting linear versus quadratic trajectories and computed the timing of the inflection points vertexwise of cortical thickness and cortical diffusivity—a novel marker of cortical microstructure—changes in 389 participants from the Dominantly Inherited Alzheimer Network. RESULTS In early preclinical AD, between 20 and 15 years before estimated symptom onset, we found increases in cortical thickness and decreases in cortical diffusivity followed by cortical thinning and cortical diffusivity increases in later preclinical and symptomatic stages. The inflection points 16 to 19 years before estimated symptom onset are in agreement with the start of tau biomarker alterations. DISCUSSION These findings confirm a biphasic trajectory for brain structural changes and have direct implications when interpreting magnetic resonance imaging measures in preventive AD clinical trials.

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.

AbstractIntroductionAlthough some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers.MethodsMutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined.ResultsMutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non‐carriers. Carriers who learned their status mid‐study had slightly higher levels of depression and lower cognitive scores.DiscussionKnowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid‐study may confer short‐term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.

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.